Student Engagement and College Grades
in Indonesian Higher Education
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Taufik Mulyadin, M.Ed
Graduate Program in Educational Studies
The Ohio State University
2020
Dissertation Committee
Dr. Tatiana Suspitsyna, Advisor
Dr. David Stein
Dr. Matthew J. Mayhew
1
Copyrighted by
Taufik Mulyadin
2020
2
Abstract
As access to post-secondary education consistently increases in Indonesia, the government and tertiary education institutions have devoted more support for students to facilitate their pursuit of academic success, represented by college grades. However, college grades remain problematic and the problem has led to dropouts among Indonesian undergraduates. The literature suggests that student engagement can serve as an antidote to the problematic learning outcomes (Kuh, 2003; McCormick, Gonyea, & Kinzie, 2013’
Zepke, 2017). Thus, this study sought to understand the effects of engagement on college grades in the Indonesian context. To achieve the goal, this study focused on three areas.
First, the study identified the relationship between student engagement and GPA. Second, the study examined the correlation of student engagement and background characteristics
(academic level, gender, major, working, and first-generation). Third, this study attempted to examine the extent to which student engagement affected GPA after controlling for background characteristics. Descriptive statistics, chi-square tests, and a logistics regression were performed in the analysis.
ii
Dedication
To my mother, Eti, and late father, Tarlan. Thank you for your love and sacrifices. To my wife, Putri, and children, Zaid, Ziad, and Zaynab. I love you all unconditionally and thank you for coming into my life.
iii
Acknowledgments
Begin Typing Here The journey of my doctoral study takes a lot of support from many individuals. For me, it has always been my family that has provided me everything necessary along the journey. First always is my wife, Putri, and little Zs, Zaid, Ziad, and
Zaynab. They were my biggest supporters and a constant source of support and love whenever I needed it. Without them, it would be so challenging for me to complete the
Ph.D. In my every difficult time, my wife reminded me of the time and effort we had already dedicated to claiming the once in a lifetime opportunity of pursuing a doctoral degree in the United States. Her reminder always encouraged me to keep going forward to the finish line of my academic journey. We believe that we are not perfect, but at least we are getting better. I love you and I am so grateful for having you during the process.
Next, I want to acknowledge my parents and my brother. My parents, Eti and
Tarlan, were incredible figures who gave a positive influence and inspired me to go to graduate school. Although my mother never went to college, she often told me about the importance of college and how it would open more opportunities to better understand and contribute to our surroundings. I would like to thank my father for his advice that I had to dream and act big. Also, he always reminded me to share what I achieved and received. It has been so long since he passed away in 2008. I missed him so much and I hope I could make him proud. I also want to thank my brother, Diky. I knew the way we take care of
iv each other was not common and online communication mostly did not work for us.
However, I knew he always supported me in his unique ways.
Once starting my academic journey as a doctoral student at the Ohio State
University, I was so glad of having a great advisor, Dr. Tatiana Suspitsyna. I never finished my doctoral study without her. For me, she was more than an advisor supporting and guiding me to grow in my academic and professional career. To my wife and me, she has become a family for all of her support and cares for us in the last four years, particularly in our most struggling times. Indeed, I learned a lot from her not only about academics but also a life. I would like to express my gratitude to my wonderful committee members, Dr. Matthew J. Mayhew, Dr. David Stein, and Dr. Marc P.
Johnston-Guerrero. Your invaluable support, guidance, and feedback allowed me to pursue excellence during the process of my dissertation writing.
I also would like to thank all faculty members in Higher Education and Student
Affairs (HESA) Program at the Ohio State University for the knowledge and experiences you shared either inside or outside of the classroom. Also, I thank my cohort peers who encouraged each other during our years of college. In particular, to Kai Zhao, I thank you for your generous support and advice so that I could survive and finish this degree. Good luck with your upcoming professional endeavor.
I am indebted to friends and families in Columbus, Ohio, who have each supported my family and me in unique and impactful ways. My family and I did enjoy every second we spent with all of you. I also cannot forget to thank my colleagues of
Antek-Antek Amerika, Dr. Dion Ginanto and Dr. Kristian Adi Putra. Both of you were an
v inspiring figure who dragged me to many opportunities we could enjoy together.
Surprisingly, our absurd conversation was so entertaining and able to overcome the tiredness I had during this long Ph.D journey. I believe one day we can lead the nation and make it greater.
vi
Vita
June 2005 ...... Al-Ihsan Islamic Boarding Shcool
April 2010 ...... B.S. English Language Education,
Universitas Pendidikan Indonesia
July 2013 ...... Staff of Vice Rector for Planning,
Research,
and Development, Universitas
Pendidikan Indonesia
August 2014…………………………………………M.Ed. Educational Studies, Ohio
University
December 2015 ...... Staff of Vice Rector for Academic
and Student Affairs, Universitas
Pendidikan Indonesia
December 2015 ...... Faculty Assistant, Educational
Administration Program, Universitas
Pendidikan Indonesia
December 2019 ...... Ph.D Educational Studies, the Ohio
State University
vii
Publications
Mulyadin, T et al. (2017). Disparity in higher education. In S. Dueker, et al (Eds),
Educational inequality in Ohio. Columbus, OH: EHE RMC.
Mulyadin, T. (2019). Difable dan Pendidikan Tinggi: Pelajaran dari Amerika Serikat
[Student with Disabilities in Higher Education: Lesson from the United States]. In
P. Rufaidah (Ed), Bunga Rampai Pemikiran Pelajar Indonesia di AS.
Washington, DC: Embassy of the Republic of Indonesia.
Fields of Study
Major Field: Educational Studies
Specialization: Higher Education and Student Affair
viii
Table of Contents
Abstract ...... ii Dedication ...... iii Acknowledgments ...... iv Vita ...... vii List of Tables...... xi List of Figures ...... xiv Chapter 1. Introduction ...... 1 Problem Statement ...... 4 Purpose of the Study ...... 7 Significance of the Study ...... 8 Definitions of Terms ...... 12 Organization of the Study ...... 14 Chapter 2. Understanding Engagement of College Students ...... 16 Higher Education in Indonesia ...... 16 Current Indonesian Higher Education ...... 18 Access, Distribution, and Completion ...... 34 GPA and College Dropout ...... 41 Undergraduates’ Engagement ...... 43 Student Engagement ...... 47 Theoretical Frameworks of Student Engagement ...... 50 Student Engagement and Educational Outcomes ...... 60 Student Engagement and Individual Factors ...... 62 Student Engagement and College Grades ...... 69 Grade Point Average (GPA)...... 69 Student Engagement and GPA ...... 72 An instrument to Measure Student Engagement ...... 80 The National Survey of Student Engagement ...... 80 Criticisms of the National Survey of Student Engagement ...... 86 Culture and Educational Activities ...... 89 Summary ...... 95 Chapter 3. Research Methodology ...... 98 Research Questions ...... 98 Data Source ...... 99 Validity and Reliability ...... 103
ix
Data Collection ...... 105 Study Variables ...... 106 Independent Variables...... 106 Dependent Variable ...... 113 Analytical Methods ...... 113 Preliminary Analysis ...... 113 Primary Analysis ...... 115 Limitations ...... 117 Summary ...... 119 Chapter 4. Results ...... 121 Preliminary Analysis...... 121 Primary Analysis ...... 127 Participants’ Grades and Demographic Characteristics ...... 127 Research Question 1: Relationship Between Student Engagement and GPA among Indonesian Undergraduates ...... 128 Research Question 2: Relationship Between Student Engagement and Student Background Characteristics among Indonesian Undergraduates ...... 130 Research Question 3: The Impact of Student Engagement on GPA Among Indonesian Undergraduates After Controlling for Their Background Characteristics ...... 136 Summary ...... 138 Chapter 5. Discussion and Implications ...... 141 Discussion ...... 141 Evident Association Between Student Engagement and Background Characteristics ...... 142 Less Relevant Engagement Indicators for GPA ...... 149 The Net Effects of Student Engagement on GPA ...... 154 Implication for Practice...... 161 Implication for Research ...... 168 Conclusion...... 171 References ...... 174 Appendix A. Chi-Square Tests Engagement and GPA ...... 206 Appendix B. Chi-Square Tests Engagement and Background Characteristics ...... 210 Appendix C. Logistic Regression ...... 230
x
List of Tables
Table 3. 1 Pilot ISSLA Question Items Eliminated or Adapted from NSSE ...... 101 Table 3. 2 Items within Each Engagement Indicator ...... 107 Table 4. 1 Structural Correlations among the Engagement Indicators ...... 122 Table 4.2 The Items and Indicators of Engagement in ISSLA ...... 126 Table 4. 3 Participants’ GPA and Demographic Characteristics ...... 128 Table 4. 4 Student Engagement by GPA ...... 129 Table 4. 5 Logistic Regression Results for GPA on Student Background Characteristics and Student Engagement ...... 137 Table A. 1 Chi-Square Tests Collaborative Learning and GPA ...... 206 Table A. 2 Chi-Square Tests Reflective and Integrative Learning and GPA ...... 206 Table A. 3 Chi-Square Tests Student-Faculty Interaction and GPA ...... 207 Table A. 4 Chi-Square Tests Higher-Order Learning and GPA ...... 207 Table A. 5 Chi-Square Tests Effective Teaching Practices and GPA ...... 207 Table A. 6 Chi-Square Tests Quantitative Reasoning and GPA ...... 208 Table A. 7 Chi-Square Tests Discussion with Diverse Others and GPA ...... 208 Table A. 8 Chi-Square Tests Learning Strategies and GPA ...... 208 Table A. 9 Chi-Square Tests Quality of Interactions and GPA ...... 209 Table A. 10 Chi-Square Tests Supportive Environment and GPA ...... 209 Table B. 1 Chi-Square Tests Collaborative Learning and Academic Level ...... 210 Table B. 2 Chi-Square Tests Reflective and Integrative Learning and Academic Level 210 Table B. 3 Chi-Square Tests Student-Faculty Interaction and Academic Level ...... 211 Table B. 4 Chi-Square Tests Higher-Order Learning and Academic Level ...... 211 Table B. 5 Chi-Square Tests Effective Teaching Practices and Academic Level ...... 211 Table B. 6 Chi-Square Tests Quantitative Reasoning and Academic Level ...... 212 Table B. 7 Chi-Square Tests Discussion with Diverse Others and Academic Level ...... 212 Table B. 8 Chi-Square Tests Learning Strategies and Academic Level...... 212 Table B. 9 Chi-Square Tests Quality of Interactions and Academic Level ...... 213 Table B. 10 Chi-Square Tests Supportive Environment and Academic Level ...... 213 Table B. 11 Chi-Square Tests Collaborative Learning and Gender ...... 214 Table B. 12 Chi-Square Tests Reflective and Integrative Learning and Gender ...... 214 Table B. 13 Chi-Square Tests Student-Faculty Interaction and Gender ...... 215 Table B. 14 Chi-Square Tests Higher-Order Learning and Gender ...... 215 Table B. 15 Chi-Square Tests Effective Teaching Practices and Gender ...... 215 Table B. 16 Chi-Square Tests Quantitative Reasoning and Gender ...... 216 Table B. 17 Chi-Square Tests Discussion with Diverse Others and Gender ...... 216 xi
Table B. 18 Chi-Square Tests Learning Strategies and Gender...... 216 Table B. 19 Chi-Square Tests Quality of Interactions and Gender ...... 217 Table B. 20 Chi-Square Tests Supportive Environment and Gender ...... 217 Table B. 21 Chi-Square Tests Collaborative Learning and Major ...... 218 Table B. 22 Chi-Square Tests Reflective and Integrative Learning and Major ...... 218 Table B. 23 Chi-Square Tests Student-Faculty Interaction and Major ...... 219 Table B. 24 Chi-Square Tests Higher-Order Learning and Major ...... 219 Table B. 25 Chi-Square Tests Effective Teaching Practices and Major ...... 219 Table B. 26 Chi-Square Tests Quantitative Reasoning and Major ...... 220 Table B. 27 Chi-Square Tests Discussion with Diverse Others and Major ...... 220 Table B. 28 Chi-Square Tests Learning Strategies and Major ...... 220 Table B. 29 Chi-Square Tests Quality of Interactions and Major ...... 221 Table B. 30 Chi-Square Tests Supportive Environment and Major ...... 221 Table B. 31 Chi-Square Tests Collaborative Learning and Working ...... 222 Table B. 32 Chi-Square Tests Reflective and Integrative Learning and Working ...... 222 Table B. 33 Chi-Square Tests Student-Faculty Interaction and Working ...... 223 Table B. 34 Chi-Square Tests Higher-Order Learning and Working ...... 223 Table B. 35 Chi-Square Tests Effective Teaching Practices and Working ...... 223 Table B. 36 Chi-Square Tests Quantitative Reasoning and Working ...... 224 Table B. 37 Chi-Square Tests Discussion with Diverse Others and Working ...... 224 Table B. 38 Chi-Square Tests Learning Strategies and Working ...... 224 Table B. 39 Chi-Square Tests Quality of Interactions and Working ...... 225 Table B. 40 Chi-Square Tests Supportive Environment and Working ...... 225 Table B. 41 Chi-Square Tests Collaborative Learning and First-Generation ...... 226 Table B. 42 Chi-Square Tests Reflective and Integrative Learning and First-Generation ...... 226 Table B. 43 Chi-Square Tests Student-Faculty Interaction and First-Generation ...... 227 Table B. 44 Chi-Square Tests Higher-Order Learning and First-Generation ...... 227 Table B. 45 Chi-Square Tests Effective Teaching Practices and First-Generation ...... 227 Table B. 46 Chi-Square Tests Quantitative Reasoning and First-Generation ...... 228 Table B. 47 Chi-Square Tests Discussion with Diverse Others and First-Generation .... 228 Table B. 48 Chi-Square Tests Learning Strategies and First-Generation ...... 228 Table B. 49 Chi-Square Tests Quality of Interactions and First-Generation ...... 229 Table B. 50 Chi-Square Tests Supportive Environment and First-Generation...... 229 Table C. 1 Block 1: Omnibus Tests of Model Coefficients ...... 230 Table C. 2 Block 1: Model Summary ...... 230 Table C. 3 Block 1: Hosmer and Lemeshow Test ...... 230 Table C. 4 Block 1: Classification Tablea ...... 231 Table C. 5 Block 1: Variables in the Equation...... 231 Table C. 6 Block 1: Correlation Matrix ...... 231 Table C. 7 Block 2: Omnibus Tests of Model Coefficients ...... 232 Table C. 8 Block 2:Model Summary ...... 232 Table C. 9 Block 2:Hosmer and Lemeshow Test ...... 232 Table C. 10 Block 2:Classification Tablea ...... 232 xii
Table C. 11 Block 2:Variables in the Equation ...... 233
xiii
List of Figures
Figure 2. 1 Number of Indonesian higher education institutions by institutional type in 2018 ...... 19 Figure 2. 2 Expenditure on higher education in Indonesia from 2007 to 2015 ...... 24 Figure 2. 3 Qualification of faculty at Indonesian higher education institutions in 2018 . 30 Figure 2. 4 Numbers of faculty in Indonesian higher education from 2007 to 2018 ...... 32 Figure 2. 5 Number of institutional research publications and patents from 2014-2017 .. 33 Figure 2. 6 Total enrolment in Indonesian higher education from 2014 to 2018 ...... 36 Figure 2. 7 Number of top-tier universities in Indonesia by region ...... 38 Figure 2. 8 Engagement of Indonesian first-year and senior students ...... 47 Figure 4. 1 Students with high engagement at each academic level group ...... 131 Figure 4. 2 Students with high engagement at each gender group ...... 132 Figure 4. 3 Students with high engagement at major group ...... 133 Figure 4. 4 Students with high engagement at each working status group ...... 134 Figure 4. 5 Students with high engagement at first- and continuing-generation groups . 135
xiv
Chapter 1. Introduction
Compared to the United States, Indonesian has a relatively recent history of higher education. While the establishment of higher education in the United States was firstly initiated in the 17th century (Thelin, 2011), the history of Indonesian higher education was marked by the foundation of higher education institutions by the Dutch colonialists in the early 20th century (Cummings & Kasenda, 1989). Tertiary education in the colonial era was primarily intended to train indigenous people (pribumi) in medicine, administration, and engineering as the consequence of the implementation of ethical policy and the strategic initiative to address the shortage of professionals that the colonial ruler experienced after World War I (Eng, 2004; Fahmi, 2007).
Although higher education in Indonesia is younger than that of the United States, the sector carries out impressive development and growth, particularly in terms of institutions and enrollments. Having begun with two public higher education institutions and 1,600 students in the early years after Indonesia gained its independence in 1945
(Buchori & Malik, 2004), Indonesian higher education encompassed 4,718 institutions with varied institutional types (university, institute, school, academy, polytechnic, and community college) with about 5.7 million undergraduates enrolled and over 247,000 academic personnel hired in 2018 (Directorate General of Higher Education [DGHE], 2018;
Ministry of Research, Technology, and Higher Education [MoRTHE], 2017).
1
Most Indonesians perceive higher education as a critical means to promote social mobility allowing everybody with motivation and competence to succeed by connecting individuals to desired jobs and advancing career pathways (Buchori & Malik, 2004;
Welch, 2007). Thus, although every political regime shift in Indonesia led in times of political and economic turmoil, the college enrollment rise persists. For instance, when the Asian financial crisis stroke Indonesia’s economy, which, in turn, resulted in the collapse of the New Order Regime in the late 1990s, the number of college entrants and the total enrollment at an undergraduate level continued to increase (Welch, 2007). From
1999 to 2002, the government reported an annual increase of over 5% and 6% respectively for new and entire undergraduates enrolled in Indonesian tertiary education institutions (Kintamani, 2015).
The increasing number of Indonesians entering college has encouraged the government and institutions in post-secondary education to put more efforts and sources on fostering students’ academic success. Both the government and institutions work hand in hand in identifying and implementing necessary actions to ensure students succeed in college. Allocating a higher budget, hiring more faculty, expanding facilities, and improving instructional practices are some strategies that are often carried out to allow students to be academically successful during their college years (Moeliodihardjo, 2014).
Among various measures of student success, the government and institutions frequently use a grade point average or GPA to estimate students’ success in their academic endeavors (Fahmi, 2007; Moeliodihardjo, 2014). Moreover, the inclusion of
GPA into the requirements for course, scholarship, graduation, graduate school, and job
2 eligibility reflects the importance of college grades not only during but also after college.
The significance of GPA as a proxy of student academic success also resonates in research on learning in the context of Indonesian tertiary education (Luntungan, 2012;
Siang & Santoso, 2016).
Once it comes to what affects students’ academic grades, course-related activities and instructional practices inside the classroom draw considerable attention from college practitioners and researchers. Unsurprisingly, such issues as teaching method, instructional media, online learning, curriculum, and learning content have become prioritized concerns for the government, college leaders, administrators, and faculty in their attempts to improve students’ GPAs (Sulisworo & Suryani, 2014). Researchers also are more likely to focus on educational practices related to coursework and instruction as the potential factors contributing to college grades (Hardini & Adriani, 2018; Siang &
Santoso, 2016). While course-related activities and instructional practices enjoy great endorsement from practitioners and researchers in the higher education sector, the remaining educational activities outside class and their effects on students’ GPAs are under-discussed (Logli, 2016).
The literature on college impact suggests that what students experience not only inside but also outside class during their years of study functions as a crucial determinant of their learning gains (Astin, 1999; Kuh, 2003; Pascarella & Terenzini, 2005, Zepke,
2017). Teaching practices, learning strategies, student-faculty interaction, institutional support, co-curricular activities, and interactions with other individuals on campus are examples of the factors that were often found of having an impact on college grades
3
(Kuh, 2003; McCormick, Gonyea, & Kinzie, 2013; Pascarella & Terenzini, 2005).
Therefore, understanding of effective collegiate practices both inside and outside the classroom associated with college grades becomes increasingly critical for scholars and practitioners in Indonesian higher education to optimize the full potential of college in impacting students’ academic success.
Problem Statement
As the government of Indonesia consistently grants students greater access to higher learning, their success in college has attracted a large amount of attention from both academic and administrative personnel. Indeed, the government and tertiary education institutions have provided more support for students to facilitate the pursuit of academic success (Moeliodihardjo, 2014). In Indonesian higher education, GPA has been widely used to represent academic success and thus, the state and tertiary education providers are determined to improve students’ college grades to ensure that they can persist, graduate, and then find a job or go for further studies.
Institutions in Indonesia typically require a minimum GPA of 2.00 to graduate.
However, many employers and higher degree studies have a GPA requirement target higher than that (Agustiani, Cahyadi, & Musa, 2016). To apply for positions as a civil servant or to pursue further degrees, applicants must hold a bachelor’s degree with a GPA of 3.00 or higher. Moreover, many companies or other private institutions have set a minimum grade requirement of 3.00 or 3.25 for job applicants. A higher GPA necessary for the pursuit of higher degrees and employment after graduation has encouraged undergraduates in Indonesia to seek to achieve a GPA of 3.25 or above (Siang & Santoso, 4
2016). Agustiani and colleagues (2016) revealed that the failure to reach a sufficient GPA led students to leave college since they found themselves unfit to stay in the institution and perceived that poor academic standing would be less useful for their future.
Despite increasing support with an emphasis on course-related practices and instructional activities available to undergraduates, GPA remains problematic to many of them. Imran, Susetyo, and Wigena (2013) revealed that a low GPA was the factor with the most substantial effect on students’ increasing likelihood of dropped out of college in
Indonesia. More than 286,000 undergraduates left college without a degree in the academic year of 2014-2015 alone (Ministry of Research, Technology, and Higher
Education [MoRTHE], 2015). This number constituted a slight fraction of the overall national enrollments (4.6%) but was relatively large (20%) in comparison with the total of new student entrants in the corresponding year. Given the association of GPA and college leaving, the aforementioned number of undergraduate dropouts may reflect insufficient college grades that persist among Indonesian undergraduates.
The literature on college impact on students has revealed that times and efforts students devote to learning activities as well as the college environment are associated with collegiate outcomes (Astin, 1984; Kuh, Cruce, Shoup, Kinzie, & Gonyea, 2008;
Pace, 1979, 1980, 1984; Pascarella, 1985; Pascarella & Terenzini, 2005; Tinto, 1988,
1993). These ideas of involvement, time on task, quality of effort, and campus environment constitute the concept of student engagement that emphasizes the importance of student active participation in educationally purposeful activities such as interaction with faculty, learning with peers, academic challenge, and active learning, and
5 also institutional support to promote it (Hu & Kuh, 2002; Kuh, 2001b, 2009; Kuh,
Kinzie, Schuh, Whitt, & Associates, 2010; McCormick, Kinzie, & Gonyea, 2013; Wolf-
Wendel, Ward, & Kinzie, 2009).
In college impact research, student engagement has been extensively studied for a long time, almost seven decades (Zepke, 2017). A large body of studies, with few exceptions, have empirically found the correlation between student engagement and student academic success, particularly in relation to college grades (e.g., Bai & Pan,
2009; Carini, Kuh, & Klein, 2006; Greene, Marti, & McClenney, 2008; Kuh et al., 2008;
Kuh, Pace, & Vesper, 1997; Laird & Cruce, 2009). Student engagement is perceived as a value-added process that contributes to desired academic gain.
Even though there have been a large number of studies examining the relationships between student engagement and grades in college, the majority of empirical evidence from these studies is merely relevant and applicable to the more developed higher education systems such as the United States, Canada, the United
Kingdom, Australia, New Zealand, Ireland, and China (e.g., Astin, 1993; Carini et al.,
2006; Coates, 2010; Fuller, Wilson, & Tobin, 2011; Gordon, Ludlum, & Hoey, 2008;
Hong, 2010; Hugh & Pace, 2003; Jinghuan, Wen, Yifei,, & Jing, 2014; Krause & Coates,
2008; Kuh et al., 2008; Lowe, Shaw, Sims, King, & Paddison, 2017; Maskell & Collins,
2017; Melius, 2011; Nelson, Quinn, Marrington, & Clarke, 2012; Radlof & Coates, 2010;
SHu, 2011). The impact of student engagement on grades in the nations where higher education systems remain developing and even struggling, especially within the context of Southeast Asia, such as Indonesia, relatively remains understudied. Since student
6 learning and its environment are contextually bound (Kahu, 2013), it is important to undertake more studies to gain evidence on the influences of student engagement on college grades in other than the context of the existing body of research.
Due to the problematic college grades and the limited literature on student engagement in the context of Indonesian higher education, it is critical to conduct a study on student engagement and its effects on GPA in Indonesian higher education. Hence, the purpose of the present study is to examine the impact of student engagement on GPA among Indonesian college students. The findings of this study would contribute to a better understanding of the college impact in the context of Indonesian higher education and provide practical suggestions as well as guidance for policy-makers, administrators, and college practitioners at an either governmental or institutional level to design effective educational programs leading to student academic success.
Purpose of the Study
This study sought to attain an understanding of the impact of student engagement on college grades of students in Indonesia. Student engagement reflects the time and energy students devote to academic and co-curricular activities, which is closely related to student development and gains (Kuh, 2003). Student engagement examined in this study was comprised of the ten indicators including higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment (McCormick et al., 2013; United
States Agency for International Development [USAID], 2014). 7
To achieve the goal, this study focused on three areas. First, the study aimed to identify the relationship between student engagement and GPA. Second, the study also attempted to examine whether student engagement and background characteristics
(academic level, gender, major, working, and first-generation) were correlated. Third, based on existing literature on the relationships between student engagement and academic grades (Astin, 1993; Carini et al., 2006; Fuller et al., 2011; Gordon et al., 2008;
Hugh & Pace, 2003; Melius, 2011), this study sought to understand to which extent student engagement affected GPA after controlling for background characteristics.
Significance of the Study
An increasing number of students going to college has been consistently visible in
Indonesian higher education since its first modern university was founded in the early
1900s. The growth of wealth and population coupled with the need to have not only locally but also globally competitive human resources in the country boosted access to higher learning (Welch, 2007). Due to an increasing number of college students, the government and institutions have increased their support to improve student academic success represented by GPA. However, as indicated by undergraduate dropout, many college students still cannot obtain sufficient grades. Although student engagement has been empirically considered as an antidote to remedy problematic college grades (Kuh,
2003; Kuh et al., 2008), studies on student engagement and its effects on GPA in the
Indonesian context remain limited. Hence, it is critical to conduct this study to examine the impact of student engagement on the academic grades of Indonesian college students.
8
This study will contribute to empirical and practical values to college teaching, learning, and student engagement. The findings of this study can be beneficial for scholars, faculty members, student affairs professionals, learning advisors, and students themselves for understanding and identifying educational learning practices that potentially affect GPA. Furthermore, they can use what this study found to design and manage educational initiatives to support Indonesian undergraduates and improve their collegiate experiences. Specific explanations of these benefits were discussed in the following paragraphs.
First, this study will support the government and institutions’ efforts in understanding and enhancing the quality of learning activities for undergraduates in
Indonesia. Prior studies have examined student engagement and its relationship with college grades (Carini et al., 2006; Fuller et al., 2011; Jinghuan et al., 2014; Krause &
Coates, 2008; Kuh et al., 2008; Melius, 2011; Radlof & Coates, 2010; SHu, 2011) but the studies were conducted within more established higher education systems. Hence, their results cannot fully apply to and explain student engagement in Indonesian higher education, which remains in a developing stage. In this study, the researcher takes a step forward to examine student engagement and GPA among undergraduates at Indonesian institutions. A dataset on engagement collected through the administration of the
Indonesian Survey of Student Learning Activities (ISSLA) in 2017 was utilized in this study to identify students’ involvement in educational activities in Indonesian higher education and its relationship with GPA regardless of their demographic characteristics.
The findings they study yielded will help practitioners and policymakers at both
9 governmental and institutional levels to plan, design and implement effective programs as well as educational activities for supporting college students more effectively.
Second, this study will contribute to the existing scholarship by adding an understanding of student engagement and its impact on academic grades in the
Indonesian context. The educational system, culture, and history among many other factors indeed affect student learning in a particular ecosystem of higher education
(Kahu, 2013). Hence, how student engagement is understood, developed, and implemented in Indonesia becomes unique and perhaps different from other countries where student engagement has been frequently studied, such as the United States,
Canada, the UK, Australia, and China. The findings of this study can help Indonesian colleges and universities obtain a better understanding of the engagement of their students and allow them to examine the services and resources they offer to the entire college student body.
Third, the findings from this study can be utilized by faculty to reexamine their approaches toward teaching students who are academically vulnerable and therefore less likely to academically succeed. Students in their first year in college are commonly not well informed about or familiar with classroom and academic norms on campus, such as class participation, frequent group work, critiquing the ideas of faculty and peers, and performing effective communication with faculty and advisors, which can create many challenges in college teaching and advising (Christyanti, Mustamiah, & Sulistiani,
2012). Students’ unfamiliarity with campus and its norms sometimes brings challenges and even frustration for faculty members. Greater access to higher education not only
10 increases the number of college students and in turn changes student demographics in college, but also affects the instructional methods faculty employ in class (Mayasari,
Mustami’ah, & Warni, 2012). The results from this study, particularly regarding students’ learning preferences, will allow faculty to continually reflect on their teaching behaviors, adjust pedagogies or instructional techniques, and create more inclusive learning and more engaging environments for all students with diverse background characteristics.
Fourth, the study will help student affairs professionals and learning advisors to provide better support for undergraduates in a more inclusive learning environment.
Student affairs professionals and learning advisors who work closely with students, such as academic advisors, specialists at teaching and learning centers, and those who teach undergraduate courses, are ideal audiences of this study. Its findings and recommendations will equip student affairs professionals and learning advisors with more knowledge and tools as they collaborate with faculty in supporting students in college.
Fifth, the findings and recommendations of this study will help students reflect on their own learning-related engagement behaviors. Students can become better aware of their participation in academically purposeful activities, which potentially affects their academic success in college within diverse learning environments. Then, students can establish a mutual understanding of engagement and optimally utilize what institutions offer to succeed in their academic life. More importantly, they will be able to support each other in their involvement in various educational activities, such as course projects and study groups. Mutual understanding and peer support among students are essential for promoting academic achievement during their years of study.
11
Definitions of Terms
Indonesian undergraduate students. This group of students in this study referred to students who identified themselves as a non-international student and a first- year or senior for class level. They were full-time students pursuing undergraduate degrees at participating institutions in the 2017 ISSLA. They received their primary and secondary education mainly in Indonesia.
Student engagement. Student engagement is “the time and energy students devote to educationally sound activities inside and outside of the classroom, and the policies and practices that institutions use to induce students to take part in these activities” (Kuh, 2003, p. 25). To estimate students’ involvement in educational activities, this study utilized the ten engagement indicators of ISSLA including higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment (USAID,
2014).
GPA. GPA is a measure that has been widely used to indicate college students’ academic success (Jaeger & Eagan, 2007). In this study, the student participants’ GPA was obtained from the official academic record that the institutions provided when participating in the 2017 ISSLA.
Background characteristics refer to the characteristics belonging to students when they enter and live in college (Astin, 1993). In this study, these characteristics included academic level, gender, major, working, and first-generation. 12
Academic level. ISSLA is primarily intended for first-year and senior students in college (USAID, 2014). Both student groups were included in the current study. The information about students’ academic level is generated from their self-reported responses to the item on class standing in the 2017 ISSLA.
Gender. In ISSLA, there are only two available responses to the item of gender, female and male (USAID, 2014). Hence, this study employed the responses to categorize students based on their self-reported gender in the 2017 ISSLA.
Major. In this study, students’ academic major was categorized into STEM and non-STEM. While STEM majors more focus on the enhancement of quantitative skills and use of collaborative study, non-STEM majors including the humanities, arts, and social sciences emphasize participation, interaction, interest, and exploration of ideas (Brint,
Cantwell, & Hanneman, 2008).
Working. Students who reported working either on or off-campus in the last two semesters were considered as a working student in this study.
First-generation. To determine students’ first-generation status, this study relied upon their responses on the item on an educational level both parents, mother and father, ever gained. Students whose neither parents attained tertiary education were considered as first-generation (Lee, Sax, Kin, & Hagedorn, 2004). The status of first-generation is frequently associated with lower socioeconomic status because this group of students is more likely to come from low-income families (Bui, 2002).
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Organization of the Study
The study is organized into five different chapters. The first chapter mainly discusses an introduction to the study including the background of the study, the problem statement, the significance of the study, the definitions of terms, and the organization of the study. The second chapter presents a review of the literature that is relevant to the focus of the current study. This chapter begins with an overview of higher education and preliminary findings of student engagement in Indonesia. The next section discusses the theoretical frameworks regarding student engagement, the effects of engagement on educational outcomes, and the demographic variables that impact students’ involvement in educational activities. Later, the impact of student engagement on academic grades for undergraduate students is discussed. The last section of chapter two focuses on culture and how it might shape college students’ behavior of involvement during their college years.
The focus of the third chapter is the research methodology utilized in this study.
This chapter consists of four sections including research questions, a data source, data collection, study variables, analytical methods, and limitations. The first section highlights the research questions that guided the study in achieving its goal. The section of the data source presents the information about the survey of ISSLA as a primary source of the data to be examined in this study. This information includes the history and development of ISSLA and its validity as well as reliability. The next section discusses the administration process and the participants (students and institutions) of the survey.
The following section describes a set of variables to be included in the analysis of this 14 study: academic performance, student engagement, and demographic characteristics, including academic class, gender, major, employment, and first-generation status. The last section presents descriptive, chi-square test, and logistic regression analyses as methods this study used to analyze the data. The fourth chapter highlights the results and the findings from the data analysis. Eventually the fifth chapter describes the discussions of the results, the implication of the study for practice and future research, and the conclusion of the study.
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Chapter 2. Understanding Engagement of College Students
This chapter primarily discusses the literature on engagement among undergraduates during their college years. This chapter begins with a broad picture of higher education in Indonesia. It discusses the current higher education system, including associated challenges and growth in terms of budget, teaching, research, access, attainment. The section also describes student engagement in Indonesia. The second section of this chapter focuses on student engagement. It presents the theoretical frameworks regarding student engagement, the effects of engagement on educational outcomes, and the demographic variables that impact students’ involvement in educational activities. In the third section, the impact of student engagement on academic grades for undergraduate students is described. The fourth section highlights the instrument to measure student engagement and associated criticism. The next section focuses on culture and how it might shape college students’ behavior of involvement during their college years. In summary, the gaps in the current scholarly literature and how this study would address those gaps are discussed.
Higher Education in Indonesia
It is challenging to manage higher education in Indonesia that exhibits enormous geographic, ethnic, and cultural diversity. Indonesia is the largest archipelago nation in the world with about 17,500 islands and a total area of over 7,9 million square kilometers 16
(land and sea) (Kuncoro, 2013). About 6,000 of the islands are currently inhabited by nearly 265 million people placing Indonesia as the 4th most populous nation in the world after China, India, and the United States (Indonesian Central Agency on Statistics
[ICAS], 2018). Indonesia is composed of over 375 ethnicities, nearly 700 languages and dialects, and six major religions (Islam, Protestantism, Catholicism, Hinduism,
Buddhism, and Confucianism) (Ananta, Arifin, Hasbullah, Handayani, & Pramono,
2013). Islam believers constitute over 85 % of the population and Indonesia was hence considered as the world’s biggest majority-Muslim nation (ICAS, 2018). Given this challenging environment, the government and institutions have to confront the difficult task of meeting societal needs for higher education.
Indonesia plays a vital role in the economy at both global and regional levels. At the global level, Indonesia today has the 16th largest economy and is predicted to become the seventh-largest by 2033 (Oberman, Dobbs, Budiman, Thompson, & Rosse, 2012;
Organisation for Economic Co-operation and Development [OECD], 2015). In Southeast
Asia, Indonesia’s economy considerably dominates the region (OECD, 2015). Despite the promising economy, inequality still becomes a concern in Indonesia since over 40 % of the entire population earn less than US$ 2 daily (Tadjoeddin, 2016). The economic gap has rapidly grown between rich and poor in Indonesia. Tadjoeddin (2016) asserted that four richest people are wealthier than 100 million of the poorest people in the country
To serve Indonesians’ increasing needs for education, the government continuously expands its educational system. At the global level, Indonesia currently has the fourth-largest education system after such other major nations as China, India, and the
17
United States (Welch, 2012). Interestingly, although higher education in Indonesia is relatively young, it has made remarkable progress, particularly in enrollment, access, and institution (Logli, 2016; Nizam, 2006). Nevertheless, it to some extent remains a peripheral sector (Altbach, 2016). For example, none of Indonesian higher education institutions are listed as the top 100 universities in the world by international university rankings (Altbach, 2016; Logli, 2016). Moreover, the quality of its national system of higher education is lagging behind other countries in the region, such as Singapore,
Malaysia, and Thailand (Williams, Leahy, & Jensen, 2017).
The Indonesian government’s efforts are currently centered on addressing issues regarding relevance, quality, availability, access, equality, and governance of higher education. This focus was reflected in the ten-year plan published by the Directorate
General of Higher Education (DGHE) in 2015 bringing reforms in Indonesian higher education through autonomy, quality, accountability, accreditation, and evaluation (Zein,
2017). These issues were also highlighted in the DGHE long-term strategy of 2003-2010.
The higher education act of 12/2012 provides more detail and comprehensive description of strategies carried out to improve institutional autonomy, quality assurance, and access and to strengthen vocational training at a college level (Moeliodihardjo, 2014; Zein,
2017).
Current Indonesian Higher Education
The higher education system in Indonesia is large and diverse. The Directorate
General of Higher Education (DGHE, 2018) reported that the number of institutions in
Indonesian higher education reached 4,718 that served nearly 5.7 million students in
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2018. These institutions can be categorized into secular or religious, public or private, and types based on academic coverage such as university, institute, college, polytechnic, academy, and community college (Moeliodihardjo, 2014; Altbach & Umakoshi, 2004).
While the first three institutional types focus on the academic areas, the other three are vocational. It needs one to four years of study to complete vocational or diploma programs, four years for a bachelor’s degree, additional two years for a master’s degree, and following three years for a doctoral degree (DGHE, 2016). As seen in Figure 2.1, private institutions considerably outnumbered that of public ones since publicly funded institutions were not accommodating enough for the increasing need for higher education
(Welch, 2012).
Figure 2. 1 Number of Indonesian higher education institutions by institutional type in 2018
19
Community college establishment and distance learning are the current developments that drew considerable attention from the government The Ministry of
Education published the strategic plan of long-term higher education development in
2011 that prescribed the establishment of at least one community college in every city and district across Indonesia (OECD, 2015). Also, the higher education act of 2012 puts more emphasis on the importance of higher education to gain trained manpower, one of which is through the presence of community colleges in all cities and districts. In 2012, the government planned to open around 500 community colleges in the near future
(Clark, 2014; Moeliodihardjo, 2014). Until 2018, 21 community colleges have been established, 6 of which are public while 15 are private (DGHE, 2018).
At community colleges, only 1 to 2 year-diploma programs are offered with the focus on vocational training in such fields as automotive engineering, nursing, electrical engineering, nursing, manufacturing, and culinary (Clark, 2014; Moeliodihardjo, 2014).
The establishment of these institutions needs permission from the government through the ministry of higher education to warrant that it meets expected quality and finance standards. To build necessary quality during the beginning operation, community colleges received support from more reputable universities or colleges. For example, the
Bogor Agricultural University (IPB) facilitated the establishment of four community colleges located in different districts.
Distance education has recently become a buzzword in Indonesia's higher education. An increasing number of both public and private institutions expressed their interest in distance education (Soekartawi, Haryono, & Librero, 2012). In Indonesia,
20 distance education is a critical and relevant approach to reach those who were previously underserved in higher education (Jacob, Wang, Pelkowski, Karsidi, & Priyanto, 2012).
Distance education aims to provide higher education for those who are unable to attend a traditional college setting due to geographical, personal, or work constraints. However, the development of distance education with the focus on vocational training was primarily driven by the market orientation together with politics to meet the growing demand for skilled workers through efficiency, scrutiny, standardization, and autonomy of higher education (Mason, Arnove, & Sutton, 2001). Consequently, this type of education heavily focuses on producing trained graduates over generating as well as disseminating knowledge.
Like in other Southeast Asian countries, Indonesia made a distance education debut in the area of teacher training to boost the number of teachers and instructors that, in turn, significantly impacted national development in many sectors such as economy, democracy, and society (Soekartawi et al., 2012). The National Center for Teacher
Training and Development by Correspondence (PPPG) was firstly opened in 1950 while the National Center for Technology and Communication for Education (PUSTEKKOM) in 1974. These two institutions focused on teacher training. Later, to support distance education through research, training, and development, other institutions including The
Indonesian Distance Learning Network (IDLN) along with the Southeast Asian
Ministries of Education Organization Regional Open Learning Center (SEAMOLEC) were established in the early 1990s.
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The last institution that rightfully runs and offers distance education is the Open
University or Universitas Terbuka. In 1984, this institution was launched in 1984 to better enroll the growing number of students living in distant areas. However, the majority of students at the Open University are workers with a full-time position who are least likely to attend conventional college classrooms (Afzal, Pribadi, Setiani, &
Chandrawati, 2017). The Opening University offers a small number of academic programs at a graduate level and nearly 50 programs at an undergraduate degree across five different schools, including education, mathematics and natural sciences, law, politic, and social sciences, and economics. The Open University (2018) reported that in
2018 it enrolled over 300,000 undergraduates and more than 2,000 graduate students.
Previously, the Open University was the only institution that the government assigned to manage and offer higher education through distance learning. Today, the government also permits other higher education institutions to provide distance education
(Afzal et al., 2017). Due to the increasing development and use of technology in
Indonesia, distance education via online learning is expected to progress as well as expand. To improve student learning, institutions have benefitted from technology use in providing such supports as academic tutorials, advising, and counseling services. Course materials are increasingly documented and shared digitally. The advantages of technology allow any institutions or programs to conduct distance education.
Technology advancement potentially leads distance education to experience remarkable expansion in Indonesian higher education. However, there are substantial challenges that might prevent potential distance education expansion from occurring. For
22 instance, quality internet connections were limitedly available in big cities or large institutions (Jacob et al., 2012). Some other barriers in distance education are associated with supportive infrastructures, qualified human resources, quality assurance, the dominant view that graduates from traditional education perform highly better than those receiving distance education, and organized coordination among distance education providers. Although the challenges remain present in today’s distance education, it displays increasing progress (Chapman & Sarvi, 2017).
The number of students, academic programs, and institutions in the distance education sector consistently increases. This increase is expected to be further boosted by the current enactment of ministerial act that prescribes and allows public and private institutions respectively to conduct online learning (Hendayana, 2019). It is the government’s effort to anticipate the fourth industrial revolution (Industry 4.0) by increasing access to higher education that potentially leads to human resource quality improvement. Moreover, compared to those receiving traditional education, graduates from distance education were reported to have relatively equal performance in the workforce and higher degree studies (Chapman & Sarvi, 2017; Jacob et al., 2012)
Financing Higher Education. Indonesia has demonstrated unwavering commitment and great attention to education. It is reflected in the constitution that requires the government to allocate at least 20 % of the respective budgets to education since 2000 (Adam & Negara, 2015). However, the obligation had not been fulfilled by the government until 2009. Between 2000 and 2007, the expenditure allocated on education ranged from 6 to 16 % in Indonesia, while neighbor countries, such as
23
Malaysia, Singapore, and Thailand, spent more (Soedijarto, 2009). The World Bank
(2018a) reported that during the last decade, from nearly 11 to over 17 % of the
Indonesian government spending on education went to higher education (see Figure 2.2).
percent of total expenditure on percent of education GDP
Figure 2. 2 Expenditure on higher education in Indonesia from 2007 to 2015
In Indonesian higher education, grants to fund institutions are primarily sourced from public funding, tuition, private enterprises, auxiliary services, and philanthropy
(Adam & Negara, 2015; Moeliodihardjo, 2014). To facilitate increasing enrollments and improving the quality of higher education, the government consistently increases the expenditure on this sector. For instance, the government allocated nearly USD 1 billion
(USD 1 = IDR 13,000) in 2007 and this amount tripled in 2014 to over USD 3 billion
24
(OECD, 2015; The World Bank, 2019). This increase is also relatively reflected in government spending on higher education as a share of gross domestic product (GDP), from .17 % in 2007 to .51 % in 2015. In 2012, the government applied a new regulation that the budget distribution to public institutions was determined by a set of considerations that include geographic location, study program, enrollment, and affirmative action program (Adam & Negara, 2015). To access the government fund, it begins with the budgetary proposal submission to the ministry and ends with the decision by the legislature.
While public institutions are funded primarily through public expenditure, private institutions heavily rely on tuition fees. However, they are still eligible to receive funds from the government. At least 8 to 10 % of the total budget on higher education is allocated to private institutions through such subsidies as faculty incentives, research grants, learning equipment, and professional development (Moeliodihardjo, 2014). The government assigns (civil servant) faculty to teach at private institutions when the need for instructor arises. Also, the government grants administration and teaching staff at private institutions scholarships to further their education.
Private institutions can set their tuition fees for all programs and courses. On the contrary, public institutions do not possess this such authority. At these institutions, tuition fees are fixed and centrally determined by the government except for the autonomous institutions, most likely top research public universities, that have the power to set their own tuition rates but still need the approval from the government (OECD,
2015). Although public institutions can generate revenue, they should deposit it to the
25 government through the State Treasury. They are only allowed to spend generated revenue after receiving government consent from the Ministry of Finance (Adam &
Negara, 2015; OECD, 2015). This regulation does not apply to the autonomous institutions because they have been granted authority by the government not only to generate but also to manage revenue. At public institutions, government support remains inadequate to meet their needs (Adam & Negara, 2015; Hawkins, 2011). For instance, at some large institutions, government support only accounts for less than one-fifth of the total institutional budget (Moeliodihardjo, 2014).
The low government support in financing higher education in the last past decade has resulted in the increasing reliance of institutions on tuition fees. On average, about 30
% of annual spending should be dedicated by a household to pay higher-education costs of its members (Suryadarma & Jones, 2013). For instance, in 2018, an average student’s expenses of higher education for one year ranged from nearly USD 800 to about USD
2,000 at public institutions, while the cost at private institutions ranged from USD 1,500 to USD 4,000 (Harususilo, 2018). Meanwhile, in the same year, GDP per capita in
Indonesia was nearly USD 4,000 (Putra, 2019). Moreover, special entrance into admission has been offered at some larger public institutions to applicants who can afford higher fees without taking into account their results of entrance tests (Welch, 2012;
Wicaksono & Friawan, 2011). This entrance type might cost applicants about USD 2,000 to more than USD 20,000.
The rise of tuition fees in college, especially at public institutions, generated considerable attention and widespread criticism (Moeliodihardjo, 2014). Due to intense
26 public disapproval of college tuition hike, the regulations issued by the legislature and the government through the Ministry of Education and Culture Affairs in 2012 and 2013 respectively set a limit of revenue generated from tuition fees to 30 % of the total institutional budget at any public institutions, while it did not apply to non-regular and graduate study programs. Although the government has managed efforts to ensure the regulation implementation takes place at institutional level, the practice remains and the increase in tuition fees, hence, persistently occurs (Logli, 2016). The government also put another approach in place to make higher education affordable. In 2012, all-in-one-tuition costs, so-called single tuition fee (Uang Kuliah Tunggal), were introduced by the government at public institutions (Ahmad, 2018). With this new scheme, the tuition fee that students should pay ranges from USD 0 to USD 1,000 for each semester based on their family income. Until 2015, single tuition fee has been implemented at autonomous universities and a limited number of other public institutions. The ministerial act was issued in 2015 that prescribes the implementation of single tuition fee at every publicly funded institution. Since then, at public institutions, students from low-income families pay less while those from families with higher incomes spend more on tuition fees.
To increase public participation in higher education, the government has allocated higher budget amounts for scholarship. At least there are three scholarship types that are currently offered to undergraduate students (Moeliodihardjo, 2014; OECD, 2015). First,
Bidik Misi is scholarship that targets students from low-income families with an outstanding high school academic record. This scholarship covers tuition fees and living allowance for four years in college. Second, study assistance (BBM) and academic
27 achievement improvement (PPA) scholarships are dedicated to enrolled students on the basis of family income and academic or non-academic achievement. These scholarships are valued at USD 750 per academic year to support the recipients’ education. Last, the scholarship is awarded to student winners of the International Science Olympiad (ISO).
The amount of this scholarship award varies based on the winners’ positions. The private sector, such as companies and foundations, also provides scholarships to students enrolled at either public and private institutions (Logli, 2016).
Instruction and Research. At a relatively young age, Indonesian higher education has enjoyed rapid growth, particularly enrollment and institution
(Moeliodihardjo, 2014). However, this growth outpaced institutions’ capacity in providing quality service including research, teaching, learning support, and facility
(Mason et al., 2001; Welch, 2012). Qualified instructors, higher research productivity, and equipment and infrastructure availability are critical factors to maximize the potential of growing higher education in Indonesia. Nevertheless, these need a bigger budget and an effective institutional organization, both of which remain inadequate in many higher education institutions (Logli, 2016; Soedijarto, 2009). The majority of institutional expenses is typically spent on salaries and routine activities and services and, therefore, other substantial needs are left underserved and underfund (Liem, 2016; Moeliodihardjo,
2014; Welch, 2012)
Although the current law on higher education allows public institutions to hire their own employees, most of the faculty and administration personnel at public institutions are state employees or civil servants. All civil servants have to comply with
28 the law on civil service and centrally organized by the government through the National
Civil Service Agency (Logli, 2016). Based on the law, this agency represents the government with the authority for hiring, promotion, and termination of employees.
Mobility for civil servants is a concern since they should go through a long bureaucratic process to acquire a permit to work at different institutions. It requires the same process for civil servants to get promoted to a higher rank or status, regardless of their actual works. To address this problem, the government issued a law on Civil Apparatus in 2012 that granted public institutions the authority to hire and manage their own faculty and administration personnel, including through collective employee recruitment across institutions to promote equitable distribution and quality of human resources in higher education. However, at the implementation level, many public institutions are more likely to recruit their alumni and manage promotions on the basis of seniority over merit
(Pincus, 2015).
Since 2005, the law on education obliges all faculty at Indonesian higher education institutions to hold a master’s degree or higher in their field from accredited institutions (Moeliodihardjo, 2014). However, today many faculty remain underqualified.
As shown in Figure 2.3, the government (DGHE, 2018) reported that over 46,000 faculty
In Indonesia still held a bachelor’s degree or lower in 2018. This number accounted for
15.8 % of the total faculty. Of underqualified faculty, over 38,569 faculty (83 %) were hired by private institutions. Compared to private institutions, public institutions hired a small proportion of total faculty with a master’s degree but had more doctorate graduates.
Faculty at public institutions typically have stronger academic records and better
29 professional networking allowing them to receive more scholarship opportunities to pursue a doctoral degree in Indonesia or overseas (OECD, 2015).
The government’s initiative, the scholarship of Indonesia Endowment Fund for
Education (LPDP), was launched in 2013 and it has significantly contributed to the growing number of master and doctorate graduates in Indonesia, many of whom are hired in the higher education sector. Despite an increasing number of faculty who hold a doctorate degree, their distribution remains inequitable (Logli, 2016). In 2015, more than
60 % of faculty with a doctorate degree worked at public institutions located in Java and a few top research institutions in Indonesia (OECD, 2015).
Figure 2. 3 Qualification of faculty at Indonesian higher education institutions in 2018
As seen in Figure 2.4, between 2007 and 2012, while enrollments considerably hiked, a number of faculty consistently dropped in Indonesian higher education (DGHE, 30
2017, 2018; OECD, 2015). This problematic situation has resulted in a relatively high student-to-faculty ratio of 32 in 2012 (OECD, 2015). Since 2013, more faculty have been employed at both public and private institutions. As a result, the student-to-faculty ratio became slightly better to 28 in 2018 (DGHE, 2018). Also, faculty in Indonesia are inadequately remunerated compared to those with similar degree level working in other.
For instance, in 2016, over half of faculty members earned less than USD 300 monthly
(Rakhmani & Siregar, 2016). Due to the low salary, many faculty seek for additional alternative income by working off-campus as, for instance, a consultant for government projects at either national or local level and corporates. More faculty also teach at multiple institutions, even as they have a permanent academic position at their home institutions. Faculty tend to allocate more of their time for working off-campus instead of teaching and performing research at home institutions (Altbach & Umakoshi, 2004;
Logli, 2016; Mukimin, Habibi, & Prasojo, 2019). Consequently, students do not have adequate opportunity to interact as well as discuss with faculty outside the classroom and to receive necessary support as well as assistance for learning.
At the institutional level, the stakeholders, students and faculty, in college have made efforts to enhance diversity, particularly within teaching and learning activities.
Their concern regards the dominance of Javanese and Muslim college students in the student body across institutions. To make campus population more diverse, they encourage institutions as well as the government to recruit more students and faculty from other than Java, implement secular curricula with relevant and interdisciplinary content, promote tolerance, expand as well as diversify the program of community
31 service, mainstream inclusive and progressive learning into classroom, and manage intervention strategies to educate fundamentalist student groups that do not demonstrate tolerant and inclusive values (Logli, 2016; Moeliodihardjo, 2014).
Figure 2. 4 Numbers of faculty in Indonesian higher education from 2007 to 2018
In the last few years, the Indonesian government successfully increased the higher education institution’s productivity in terms of international research publications and patents (Moeliodihardjo, 2014; Rakhmani & Siregar, 2016). The government (DGHE,
2017) reported from 2014 to 2017, the number of research manuscripts that Indonesian faculty internationally published considerably increased over 150 % (see Figure 2.5). In
2017, compared to other Southeast Asian countries, Indonesia with 15,149 published papers ranked 3rd in publication productivity after Malaysia and Singapore. The remarkable rise was also reflected in patents. Between 2014 and 2017, the number of
32 patents awarded to faculty increased over 200 %. The rise of research publication and patent in Indonesian higher education was a result of the government’s such initiatives as incentives, a higher budget, simplifying bureaucratic process, international collaboration, diaspora involvement, intense assistance, and program expansion (DGHE, 2017; Logli,
2016).
Figure 2. 5 Number of institutional research publications and patents from 2014-2017
Although Indonesian higher education has produced higher research outputs in the last few years, their numbers remain low as compared to the faculty number. For instance, in 2017, the number of publications (15,419) and patens (4,303) only counted for about 6.31 % and nearly 1.8 %, respectively, of total faculty, most of whom are instructors at public institutions (DGHE, 2017). Inadequate public expenditure allocated
33 for research partially contributed to the relatively low research outputs resulted from higher education institutions (Moeliodihardjo, 2014). Indonesia has increased its spending on research almost five times from USD 2 billion in 2013 to USD 9.88 billion in 2017 (UNESCO, 2017). However, the government support constituted one-third of the total expenditure on research or only .1 % of GDP in 2017 lagging far behind such neighboring countries as Thailand (.61 %), Malaysia (1.25 %), and Singapore (2.2 %)
(UNESCO, 2017). In 2017, for research activities, higher education institutions received about USD 107.69 million from the government In 2017, for research activities, higher education institutions received about USD 107.69 million from the government and this amount was a mere .01 % of Indonesia’s total expenditure on research.
According to the law, all of the higher education providers in Indonesian have to perform the three core functions (Tri Dharma) including teaching, research, and community service. With a largely diversified system, it is unwise to require entire institutions regardless of their types to implement the three functions (Logli, 2016). More institutions do not have capacities, particularly financial and academic resources, to undertake research activities. Hence, these institutions better focus their efforts and resources on teaching activities with high relevance and quality. The potential of a diversified higher education system can be fully optimized when only each institution functions based on their capacities and roles.
Access, Distribution, and Completion
The rapid expansion and massification of higher education in Indonesia resulted in a sizeable and diversified system. The system enrolled merely about 100 students
34 whose parents were Indonesians in 1930 and on its independence in 1945, Indonesia had over 1,000 students (Buchori & Malik, 2004). In 2018, nearly 5.7 million students pursued college degrees at 4,718 institutions across Indonesia (DGHE, 2018). This number reflected the gross enrollment rate of 36.3 %, in comparison to 20.4 % in 2008
(The World Bank, 2018b). Indonesia has a higher enrollment rate than such countries as
Laos (15.7 %), Vietnam (28.3 %), and Philippines (35.3 %) but far lower than Malaysia
(41.9 %) and Singapore (83.9 %).
Although the number of public institutions counted for about 9.2 % of entire institutions, they enrolled nearly 40 % of total college students in 2018 (see Figure 2.6).
Based on academic fields, only one-third of college students in Indonesia study in STEM programs (Sitepu, 2013) while the rest in social sciences and education programs. The later fields attract a significant number of students since their tuition fees and associated costs are considered more affordable.
Admission to public universities, especially the-top tier institutions, is more selective than their private counterparts (Clark, 2014). The national exam assigned for high school seniors has changed over the years and it often gains criticism for its failure to promote active learning, excessive expenses, corruption, technical problems, and insufficient validity (Sihombing, Mattangkilang, & Setuningsih, 2013). However, it is still partially utilized for college admission. Annually, nearly a half-million high school graduates participate in the national entrance examination to gain entry to public universities. This exam is very competitive that leads to an average acceptance rate of below 20 %. As failing the exam, applicants from high-income families can still get a
35 place at public universities through a special admission scheme or get enrolled at prestigious private universities, but those from less wealthy families will go to cheaper private institutions or join the labor force (Logli, 2016; Sitepu, 2013).
Figure 2. 6 Total enrolment in Indonesian higher education from 2014 to 2018
Indonesia currently enjoys a growing expansion of its higher education. However, this expansion remains both socially and geographically skewed favoring certain student groups and regions. While a tiny proportion (about 3 %) of college students are a part of the lowest 20 % of income groups, over one-third fall into the highest quantile (OECD,
2015; Susanti, 2011). Due to selectivity and better reputation of public institutions, college applicants from high-income families tend to have better preparation to take the entrance examination and better chance of acceptance. Meanwhile, their poor counterparts often end up in private institutions or the labor force (Sitepu, 2013; Susanti,
2011). In 2018, female students slightly outnumber their male counterparts, 3,072,923 36 and 2,625,071 respectively (DGHE, 2018). The higher female enrolments occurred in both private and public institutions, but in public institutions, the number difference between male and female students was large, with two-thirds of entire students were female.
Higher education institutions in Indonesia are not equally distributed, favoring
Java and Sumatra over Papua and Maluku (Moeliodihardjo, 2014; Wicaksono & Friawan,
2011). The New Order era (1966-1998), heavily promoting a centralized government, considerably benefitted Java island, where the national capital (Jakarta) is located, with stability, infrastructure, public service, economy, and education (Poczter & Pepinsky,
2016). Hence, the most massive expansion of higher education occurs in this region. For instance, in 2015, nearly two-thirds of college students went to Java island to study at over 3,500 institutions there (DGHE, 2015). Also, the government (DGHE, 2015) reported that among 66 top-tier universities, over 70 % were the institutions that operated in Java island. Students from urban regions also highly outnumber their peers from rural areas (Akita & Miyata, 2008). In Indonesia, most families living in rural areas earn lower wages and never go to college. Hence, students from these families do not have an equal opportunity to access and then succeed in higher education.
To increase access to higher education, the Indonesian government has implemented certain initiatives. First, the government enacted the law on higher education in 2012 that requires entire public institutions to dedicate at least 20 % of total new enrollments for students from less wealthy families (OECD, 2015). Hence, the law granted institutions flexibility in the student recruitment process with an emphasis on,
37 other than merit, differentiated backgrounds (Logli, 2016). Second, the government established more public institutions in certain areas that were previously underserved, such as Borneo Tarakan University in North Kalimantan, State Polytechnic of Madura in
East Java, and Timor University in East Nusa Tenggara (Moeliodihardjo, 2014). Third, the government has promoted more diversified higher education by launching community colleges and mainstreaming online learning. Last, the government has allocated larger budgets for scholarship and added more scholarship schemes.
Figure 2. 7 Number of top-tier universities in Indonesia by region
The efforts that the government has managed in the last decade resulted in greater public participation in higher education. Since 2010, Indonesian higher education
38 institutions have admitted over one million students and awarded college degrees to about
700 thousand students annually (OECD, 2015). However, many students are not able to persist in college and complete their degrees. For instance, among 6.1 million students enrolled in 2015, 286.728 students were leaving college. Of 33 provinces in Indonesia, there were three provinces with the attrition of 10 % or higher: Banten (14.87%) in the island of Java and Aceh (11.11%), Bangka Belitung (11.32%), and Lampung (10.11%) in the island of Sumatera (MoRTHE, 2015). The number of college dropouts decreased in
2017 to be 195,176 students or 2.8% out of a total of 6.9 million students (MoRTHE,
2017). This year, all provinces in Indonesia performed better by having an attrition rate below 10 %. Unfortunately, in these government records, the information on the class level was not included. Hence, it was seemingly less possible to identify precisely which year college students were more likely to leave the institution.
At the institutional level, the number of dropouts is relatively high. Imran,
Susetyo, and Wigena (2013) revealed that at a four-year public institution, Bogor
Agriculture University, every year more than 300 undergraduate students did not return to the institution for their second year. This number accounted for about 10 % of the total freshmen admitted to the institution in the corresponding academic year. The higher dropout rates occur in distance learning colleges or programs. In a study by Saefuddin and Ratnaningsih (2008) focusing on undergraduates majoring in management at Open
University, the largest public distance learning college in Indonesia, about 2,543 students or over 85 % of the total enrollments in the program left the institution for the 2000/2001 academic year.
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In Indonesian higher education, female students and students from rural areas are the student groups who have more likelihood of leaving college without a degree (Zein,
2017). Students from low-income families are also more exposed to the risk of dropping out (Zein, 2017). During college, they more often find limited sources, lack of supports from parents, and insufficient financial assistance in comparison to their peers from wealthier families (Chen, 2012). Moreover, they are mostly first-generation in their families with parents who do not have any college experiences. Hence, they are less likely to receive sufficient information on higher education, such as courses, admission, and available scholarship or financial assistance, while this information is substantially critical for school choice and college planning. Due to the unavailable or limited knowledge about college, first-generation students tend not to have adequate preparation before going to college. Although the government provides full scholarship, such as Bidik
Misi, to underprivileged students, they still have to experience frequent delays in scholarship payment, while scholarship is the only source they rely on to fund their academic (Zein, 2017). Expanding access to underprivileged students is critical, but it is more important to provide them with sufficient information and necessary support before getting admitted into college and during their studies in college.
The presence of a high number of college dropouts implies that student persistence in Indonesian higher education remains problematic. Regrettably, this problem has not sufficiently gained attention from either the government or institutions.
Zein (2017) revealed that the lack of care towards the dropout problem in Indonesia was signaled by the limited availability of data and the marginal presence in the government
40 or institutions’ documents. From her search of relevant data on college dropouts, Zein
(2017) ended up with merely two sources, a center at Padjadjaran University and the government. Based on her analysis, the data produced by the latter entity was perceived as the only one that provided virtually accurate information in regards to non-completion rates. However, the data was limited to dropout rates at national and provincial levels.
GPA and College Dropout
The college dropout is substantially influenced by the interplay between students’ characteristics and interactions with campuses (Tinto, 1988, 1993). In his theory of student departure, Tinto (1993) asserted that there were three categories of the individual characteristics critical to students’ decision-making to remain in or leave college: (a) individual attributes, such as gender, race or ethnicity, and nationality; b) pre-college experiences, such as high school academic achievement and experiences; c) family background, such as shared values, socioeconomic status, and expectation as well as social climates. Other than the factor of individual characteristics, Tinto (1993) highlighted the importance of integration to the institutions to predict student leaving. He further stated that when students were socially and academically integrated into their college environment, they became more committed to their studies as well as institutions and more likely to complete their degrees. According to Tinto (1975), there were different types of attrition behavior, including academic failure, voluntary withdrawal, permanent dropout, temporary dropout, and transfer (Tinto, 1975).
In the context of Indonesian higher education, more withdrawals are voluntary and forced for students’ failure to achieve sufficient college grades and to meet the
41 academic or administrative requirements respectively (Imran et al., 2013). Under Tinto’s
(1975) attrition types, academic failure and permanent dropout occur more often in college leaving in Indonesia. A lower academic grade was conceived as one of the major causes of undergraduate withdrawals in Indonesia (Hari, Komalig, & Langi, 2018; Imran et al., 2013; Saefuddin & Ratnaningsih, 2008). Hari and colleagues (2018) sought to seek the factors that impacted student departure in the undergraduate mathematics program at
Sam Ratulangi University, a four-year institution in eastern Indonesia. In this study, the factors to be examined were focused on students’ background (gender and origin) and academic achievement (GPA). From the analysis, this study yielded that while students’ background did not display significant correlation, the academic factor was found significantly correlated to the college withdrawal. This correlation was negative which meant a declining GPA went along with a rising potential to withdraw from college.
In another study, Imran et al. (2013) attempted to study what led to the college dropout at the institutional level. They utilized the dataset of enrollments from 2009 to
2012 at Bogor Agriculture University. From the dataset, they found that more dropouts were male and first-year. Regression analysis was undertaken to identify the impact of academic achievement represented by GPA and individual characteristics such as gender, admission type, parental education as well as occupation, and family income on the college dropout. The results of the study indicated that GPA was the mere factor that significantly explained the attrition at the institution.
Different from the previous two studies undertaken in traditional institutions where physical attendance on campus was mandatory, a study by Saefuddin and
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Ratnaningsih (2008) focused on undergraduates at a distance learning institution, Open
University. In comparison with traditional institutions, distance learning colleges typically have a higher dropout rate. Moore (2016) suggested that the dropout rates of 30 to 50 % were considered relatively low for a distance learning institution. With this standpoint, in the 2000/2001 academic year, the management department at Open
University suffered from an excessive dropout rate with over 85 % of the total of nearly
3000 students leaving the institution (Saefuddin & Ratnaningsih, 2008). This retention problem inevitably drew attention from Saefudding and Ratnaningsih (2008) to study this issue. In the study, they used a dataset of 2,989 undergraduates majoring in management at Open University. They found that more dropouts were males, working students, older students, and students with a GPA of 3.00 or lower. The data was then analyzed by using regression to seek to which extent the academic variable (GPA) and the background variables (marital status, gender, age, residence, study field, temporary leave, and working) affected student departure. The results indicated that GPA, gender, age, temporary leave, and working were significant predictors of withdrawals in the management department at Open University. Among these predictors, GPA had the most substantial effect.
Undergraduates’ Engagement
The problematic GPA leading to many dropouts in Indonesian higher education has driven the need for a measure of assessment to examine the factors that meaningfully contribute to student academic success (Logli, 2016; Zein, 2017). The results from this assessment would serve as a basis for the Indonesian government
43 and higher education institutions on policymaking and program development to improve students’ academic gains.
Typically, Indonesian higher education institutions employ the survey of student satisfaction that emphasizes instructional activities in the classroom for their institutional assessment (Fahmi, 2007). This measure too narrows down the comprehensiveness of learning venues for students, including inside and outside the classroom. To make the institutional policies and programs more relevant and effective, it is highly necessary for the institution to do an assessment that examines student engagement in educational activities more comprehensively during the college years. Therefore, the Indonesian government working with USAID viewed a measure on student engagement with its broader scope of student learning spaces and forms worth being implemented and could be a guidance for tertiary education institutions in Indonesia in their policymaking and program development intended for students (USAID, 2014).
In student engagement, the coverage of educational activities is considerably broad, including studying and the amount of time and efforts allocated to it, interactions with peer students and faculty in a substantive way, and utilization of support and resources provided by the institution. Primarily, student engagement has two components: (1) student participation in various educationally purposeful activities and (2) institutional support and resources provided towards the improvement of students’ development and success (Kuh, 2009). Under this framework of student engagement, in 2013, the Indonesian government and USAID
44 under the project of Higher Education Leadership and Management (HELM) began to develop and implement the Indonesian Survey of Student Learning Activities
(ISSLA) to assess students’ participation in experiential learning across classroom and co-curricular activities (USAID, 2014). This survey is primarily intended for first-year and senior students since they are those who are most likely to face challenges and then leave college. With the inclusion of broad scope and variety of educational activities, ISSLA is expected to be able to gain substantial information on student engagement in educational activities and its potential impact on various learning outcomes allowing them to remain in college, complete the degree, and then get the desired job or pursue post-graduate study.
ISSLA’s first administration took place in 2013 and involved 680 undergraduate students in three Indonesian higher education institutions, including a public university, a private university, and a polytechnic. The student participants were selected at convenience and they completed the ISSLA survey with the paper-and-pencil mode. 54 % of the survey participants were first- year and 46 % were fourth-year. The number of female participants was higher than males, 58 % and 42 % respectively. They studied in varied study programs from art and humanities to science and many of them had higher academic attainment. The survey consisted of a set of questions about students’ demographic information, parent educational attainment, learning gain, college grades, and student engagement. There were 47 items of the survey constituting the ten indicators of engagement: higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse
45 others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment. A score for each indicator ranged from 0 to 60.
As shown in Figure 2.2, and senior participant students had the highest engagement in quality interactions with other students, faculty, academic advisor, and administrative staff. However, both these student groups were least engaged in student-faculty interactions where they discussed academic and non-academic related topics with college instructors. These findings occurred across the institutions but the private. Of ten engagement indicators, senior students had lower engagement compared to their freshman counterparts on two indicators, learning strategies and supportive environment. However, it was only the last where the engagement difference between these two student groups was statistically significant. It suggested that freshmen received more attention and support from the institutions than their senior counterparts (USAID, 2014).
The first administration of ISSLA more focused on examining the engagement level of Indonesian undergraduate students on ten indicators measured across different institutional types (USAID, 2014). It covered limitedly descriptive statistics that mainly summarized a given data set and did not try to apply the findings to the population that the sample might represent. Therefore, this study involved inferential statistics that used the data to learn about the population that the sample of data was thought to represent.
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Figure 2. 8 Engagement of Indonesian first-year and senior students
Student Engagement
In recent years, student engagement has been drawing considerable attention from researchers and practitioners in higher education as an antidote to problems related to student success (Bryson, 2014; Trowler, 2010). A long time before the popularity of student engagement was sparked in the early 2000s as the National Survey of Student
Engagement (NSSE) was firstly launched in the United States, later followed by its implementation across the globe, this concept had been developed since the 1930s (Kuh,
2003, 2009). The thriving presence of student engagement in literature is a result from the long-term efforts carried out by certain theorists and educational researchers, such as
Tyler (1937), Pace (1980), Astin (1984), Tinto (1987), Chickering and Gamson (1987),
Pascarella (1985), and Kuh (2003).
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Despite its popularity and long-standing development, student engagement suffers from a lack of agreement on the definition and application. Based on the existing literature, student engagement is theoretically categorized into cognitive, emotional, and behavioral perspectives which heavily focus on students’ activities, feeling, learning, and sense-making in educational experiences during in college (Fredricks, Blumenfeld, &
Paris, 2004; Harper & Quaye, 2008; Harris, 2008; Solomonides, Petocz, & Reid, 2012;
Trowler, 2010). Based on the cognitive perspective, student engagement is comprised of two main ideas, effort investment and strategic learning (Fredricks et al., 2004). Students who are cognitively engaged are more likely to be intrinsically motivated, perceive challenges as an opportunity to develop and be resilient, manage and organize their time as well as effort on assigned tasks in a more strategic way, and perform beyond the requirements. Student engagement with the emotional perspective puts emphasis on inclusion of affective responses, such as interest, enjoyment, satisfaction, boredom, fear, and anxiety, toward the environment and these responses are considered crucial to establish relationships between students and institutions and affect students’ motivation to learn (Harper & Quaye, 2008; Fredricks et al., 2004). Under the behavioral perspective, student engagement is meant as participation of students in a variety of learning activities and is considered as playing an important role in enhancing academic achievement as well as persistence to graduation (Hu, 2011; Kuh, 2009; McCormick et al., 2013).
Ideally, student engagement is viewed as a holistic concept in which cognitive, emotional, and behavioral dimensions are dynamically interconnected within a student
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(Fredricks et al., 2004). This holistic concept will provide a better understanding of what, how, and why students are engaged and their engagement, in turn, affects educational outcomes, such as academic achievement and persistence to graduation. This concept asserts that within an individual, each of the dimensions may vary in terms of period and intensity (Trowler, 2010). Perhaps students have higher or positive engagement on one dimension but are less or negatively engaged on the other dimension. For example, a student is less satisfied with material taught in the class but still attends and participates in all of its sessions merely to meet the requirements. This student may be considered more behaviorally engaged but less emotionally engaged. Without the holistic concept of student engagement, it would be difficult to comprehensively explain this such case.
Kuh (2003) offered a definition of engagement that attempted to integrate cognitive, affective, and behavioral dimensions and emphasized the shared responsibility between institution and student to promote as well as enhance engagement. He defined student engagement as “the time and energy students devote to educationally sound activities inside and outside of the classroom, and the policies and practices that institutions use to induce students to take part in these activities” (Kuh, 2003, p. 25). This definition asserts that student engagement arises in a learning environment where students are active participants and not only passive recipients of their education (Astin, 1993;
Chickering & Gamson, 1987; Kuh, 2003; Pascarella & Terenzini, 2005; Tinto, 1993).
Student success may be defined differently by students and institutions depending upon their own perspective or institutional research focus. However, it is typically indicated by grades, graduation rates, retention rates, or student self-reports (Pascarella &
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Terenzini, 2005). Regarding student success, engagement is a critical factor that influences whether students academically succeed and persist in college to graduation, which in turn considerably relies on the extent to which they participate in educationally purposeful activities. (Kuh, Kinzie, Buckley, Bridges, & Hayek, 2007). Therefore, once students actively participate in educational activities, they are more likely to succeed in college that their less engaged counterparts. Therefore, as students actively take part in learning activities, they are more likely to have better academic achievement and persistence to graduation than their less-engaged peers.
Primarily, student engagement has two components (Kuh et al., 2010; Wolf-
Wendel et al., 2009). The first component is student participation in various learning activities inside and outside the classroom that have an essential impact on desired outcomes. The second component is institutional support and resources provided towards the improvement of students’ development and success. In student engagement, the coverage of educational activities is sufficiently broad including learning and the amount of time and efforts devoted to it, interactions with peer students, faculty, and staff, problem-solving tasks, community service and utilization of support and resources provided by the institution (Hu & Kuh, 2002). In other words, engagement comprehensively reflects student experience in college by including the academic, non- academic, and social aspects.
Theoretical Frameworks of Student Engagement
Student engagement has been built upon certain previous theoretical models. The premise of engagement has been discussed in the literature for over seven decades, but its
50 construct has continuously evolved (Astin, 1993; Kuh, 2003; Pascarella & Terenzini, 2005;
Pace, 1984; Tinto, 1988). The research literature regarding student engagement was mostly rooted in sociological, psychological and educational theory. One of the earliest works dating back to the 1930s was that of Ralph Tyler, an education psychologist, who revealed the positive impact of time-on-task on student learning (Merwin, 1969). In other words, the increasing amount of time a student spent learning would, in turn, result in positive academic outcomes and learning. Pace (1984) later expanded the concept to show that educationally purposeful activities led to more gains. He added the dimension of quality of effort by the student. Astin (1984, 1999) later developed the theory of involvement built upon the concept of quality of effort. His theory focuses more on individual student and institutional characteristics. Over the years, other scholars such as Pascarella (1985),
Chickering and Gamson (1987), Tinto (1988, 1993), and Kuh (2003, 2009) have explored
Distinct dimensions of time-on-task as well as the quality of effort and their influences on a variety of desired outcomes in the college setting. As the definition of student engagement has dynamically advanced over time, theories will be presented in chronological order.
Pace’s Quality of Effort. Pace (1984) further enhanced Tyler ‘s time-on-task concept with his theory of quality of effort. He asserted that learning and development occurred as students devoted their time and energies to participating in substantial learning tasks and in turn, it was their commitment to determine the amount and quality of effort to be willingly devoted. Hence, under this concept, the quality of student engagement is a critical factor for students’ growth and development in college over the opportunities for
51 engagement that an institution promoted through its policies as well as strategies and students’ mere participation in activities or services offered (Pace, 1984). He designed the
College Student Experiences Questionnaire (CSEQ) by using his theory of quality of effort to determine the activities or tasks related to student development and learning. Pace
(1984) revealed that students could more succeed in college by gaining better academic achievement and college experience as they allocated more time and effort to educationally purposeful activities such as participation in co-curricular activities, peer and student- faculty interactions, and use of college facilities (Kuh, 2009). He found that the quality of effort considerably affected academic outcomes over students’ background characteristics
(Tinto, 1993). The items from the CSEQ were later adopted and used in what is known as the student engagement survey, namely NSSE.
Astin’s Student Involvement. Astin (1993) introduced a concept of student involvement that significantly influenced the development of the construct of student engagement. The most basic assumption this concept maintains is that the more times and efforts students devote to learning activities in and out of the classroom the more learning that takes place (Astin, 1993). Building on the theory of quality of effort developed by
Pace (1984), the concept of student involvement suggests that there are certain factors within student engagement, such as time allocated to such activities as interacting with peers as well as faculty and participating in student organization or club that, in turn, are positively related to desired outcomes. One of the strongest aspects of the involvement theory is that it can effectively elaborate on the existing empirical evidence and knowledge about the impact of an environmental factor on student development and learning (Astin,
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1984). This theory is network-based and emphasizes the student relationship with the educational environment and focuses on how student input affects the quality of output.
Under his theory of student involvement, Astin (1999) suggested five postulates about involvement: a) inclusion of physical and psychological aspects of efforts to be invested by students in various learning activities; b) occurrence of involvement in a continuum way that means different students can perform different levels of involvement in a single activity and an individual student can perform different levels of involvement in different activities at different times; c) inclusion of both quantitative and qualitative aspects of involvement, for instance, the extent to which students invest their times and efforts in academic tasks can be quantitatively and qualitatively examined; d) the positive impact of student involvement on learning and development; e) the importance of policies as well as practices by an institution to promote student involvement. In addition to the five postulates, Astin (1999) revealed three other principles that were rooted in his conception model of involvement. First, a student’s mental and physical times are limited.
Therefore, the most valuable source of involvement is time. Astin (1999) referred to it as
“zero-sum game” (p. 523) suggesting that the time and effort allocated by students to other than college-related activities, such as family, friends, and jobs would take away from the time left for educationally purposeful activities. Second, time devoted translates to involvement. This principle implies that the amount of times and efforts that students invest in educationally purposeful activities determines proportionally the extent of educational outcomes gained by students. Lastly, even though the concept of involvement is similar to the psychological construct of motivation, Astin (1999) preferred the use of
53 the word involvement since this has more accountability for direct observation and measurement.
Astin (1993) also offered one of the early models of college impact, namely the input-environment-output (I-E-O) model. The foundation of the model is built upon the core views of his theory of student involvement. The I-E-O model suggests that the effect that the college setting has on students is explained by functions of a set of three components, including student inputs, educational environment and student outcomes
(Astin, 1993; Pascarella & Terenzini, 2005).
The inputs referred to the characteristics attributed to students when they initially enter college (Astin, 1993). In regard to measurement, the inputs probably include high- school academic achievement, test scores, and demographic factors such as race or ethnicity, gender, age, socioeconomic status, religion, and parental education. these inputs presumably contribute directly and indirectly to students’ educational outcomes in the college setting relying on their engagement with the institutional environment. Assessing student inputs at the time of initial entry to college is critical to better understand the impact that an institution has on students. In the I-E-O model, the environment refers to college and its components such as policies, strategies, programs, services, peers, faculty, and educational experiences to which students are exposed during their college years
(Astin, 1993). The outputs referred to skills, knowledge, attitudes, values beliefs, and behaviors students gain from exposures to an institution and its environment (Astin, 1993).
The I-E-O model is aimed to accurately examine the extent to which the college environment influences educational outcomes after controlling student input varieties. This
54 model suggests that the output is resulted from the mixing contribution of the student inputs and the college environment and posited a direct relationship between these three elements.
Tinto’s Theory of Student Departure. Initially, Tinto (1988, 1993) developed a concept with the focus on student retention and then he theorized that when students were socially and academically integrated into their college environment, they became more committed to their studies as well as institutions and more likely to complete their degrees.
This concept is aligned with student engagement. According to him, academic integration takes place when students interact with the academic opportunities created by their institutions, while social integration involves relationships and personal connections. Tinto
(1993) maintained that there are formal and informal systems in the college setting to encourage integration of both academic and social dimensions.
Under Tinto’s theory, fitting in is more important for student success than academic preparation or clearly defined goals. Therefore, the ways that students connect with their institutions and their experiences can predict their intent to leave college and level of engagement to the institution. Hence, Tinto (1993) suggested that a lack of integration is the reason why some students leave their institution or higher education. According to
Tinto (1975), there are different types of attrition behavior based on the ways whether student leaving is voluntary or forced. These types include academic failure, voluntary withdrawal, permanent dropout, temporary dropout, and transfer (Tinto, 1975). Tinto
(1988, 1993) also highlighted the potential effect of students’ individual characteristics on their commitment to academically succeed and effectively integrate into the institution at
55 the time of their initial entry. The characteristics he perceived as critical fall into three categories: (a) individual attributes, such as gender, race or ethnicity, and nationality; b) pre-college experiences, such as high school academic achievement or GPA and experiences; c) family background, such as shared values, socioeconomic status, and expectation as well as social climates.
Tinto (1988) emphasized that student leaving is actually a result of the accumulative interaction process between students and the institution where they study.
Institutional fit or isolation is often the factor that considerably determines student integration into the institution. If students cannot develop a connection and receive comfort from the institution they attend, they will lose their trust that the institution can help them to succeed and meet their educational goals. Consequently, when the causes of these conflicts between students and the institution remain unresolved, students may choose or be forced to leave college or even higher education (Tinto, 1993).
Tinto (1988, 1993) with his theory of student departure postulated that students who switch institutions would face different experiences and resources on campus and, as a result, display differences in their degrees of engagement and satisfaction. Tinto (1988) suggested that for students to successfully transition, they must first separate themselves from previous relationships. This is particularly hard for transfer students who are likely to face difficulties integrating into the new campus climate and their host institution may not facilitate integration as it usually does for incoming freshmen and current students as well
(McCormick, Sarraf, BrckaLorenz, & Haywood, 2009). Programs such as orientation and other socialization opportunities are usually only available to new and current students, and
56 transfers are often excluded. Under the framework of Tinto’s theory, student engagement is essentially the result of institutional and student efforts, and specific behaviors and environmental characteristics are the reason behind student departure. Understanding student departure theory and the role of engagement in it is essential in understanding student behavior and improving educational success.
Pascarella’s General Model for Assessing Change. Similar to some of the concepts offered by Astin (1993, 1999) emphasizing the importance of institutional contribution to student growth in college and the quality of effort concept proposed by
Pace (1984) highlighting the impact of the institution’s structural characteristics and the college environment, in addition to students’ individual characteristics, on student development as well as learning, Pascarella (1985) developed the causal model for change assessment. This model is aimed to assess the extent to which differential environment impacts student development and learning during college (Pascarella, 1985). Pascarella’s causal model highlights the inclusion of the institution’s structural characteristics and environment in assessing student growth and learning.
Pascarella (1985) asserted there were five main sets of variables that either directly or indirectly influenced student progress in college. The first set of variables includes students’ individual background characteristics, such as gender, race, and socioeconomic, and pre-college experiences, such as high school academic and social experiences. The second variable set covers the institution’s structural characteristics, such as institutional mission, enrollment statistics, and student-faculty ratio. These first two sets of variables establish the third variable set, campus environment, such as peer and student-faculty
57 interactions, learning support, and co-curricular activities. The first three variable sets are representative of the initial indications of student change prior to the inclusion of college experiences. The model then accounted for the effects of the first three variable sets on the fourth variable that captured the interactions with agents of socialization or those with whom the student interacted on a daily basis, such as faculty, peers, and staff. Then, these four variable sets impact the last variable of quality of student effort and then the entire variables were assessed for any direct and indirect effects on student change (Pascarella,
1985).
Chickering and Gamson’s Good Practices in Higher Education. Expanding previous theories of college impact, Chickering and Gamson (1987) proposed a set of principles of good teaching and learning. The principles include: 1) student-faculty contact; 2) reciprocity and cooperation among students; 3) active learning; 4) prompt feedback; 5) time-on-task; 6) high expectations; 7) appreciation for diverse talents and ways of learning (Chickering & Gamson, 1987). With this framework, they asserted that student-faculty interaction is the most critical factor that considerably determines student involvement with which students can resolve their difficulties and enhance their commitment to learning and degree completion. Chickering and Gamson (1987) asserted that faculty and students share responsibility for education improvement, but they need a supportive environment that promotes good practice in higher education. By promoting quality educational practices in college, the institution and its components, such as students, faculty, staff, facilities, and policies can be concentrated on the activities that are considerably associated with desired outcomes (Kuh, 2003). The seven principles by
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Chickering and Gamson (1987) function as a guideline for an institution, faculty, staff, and administrator to provide substantial support to improve student growth and learning. These principles have led to student engagement dialogue, research, and practice. In addition, these seven principles have been foundational in studying student engagement in the present day. The construct of student engagement that built upon the widely used survey, the National Survey of Student Engagement (NSSE), is anchored most directly to the seven principles proposed by Chickering and Gamson (1987).
Tyler, Pace, Astin, Tinto, Pascarella, and Chickering and Gamson have presented theories and models of student retention, attrition, and involvement that all share similar characteristics. As a synthesis, all of the models recognize that the student has inherent characteristics and experiences that he or she brings to the college experience. Besides, there is a consensus among the models that the institutional characteristics (such as structure, programs, services, values, and behaviors) and environment also impact student success. In turn, it is the impact or interaction between student and institutional characteristics, whether positive or negative, that determines student persistence. Student engagement is situated at the intersection of student behaviors and the college setting or environment (Kuh, Kinzie, Buckley, et al., 2007). In other words, student engagement comprises of common characteristics from these theories and models. One may also see these theories as being complementary to the concept of student engagement. Another crucial component embedded in most of the models is that the quality of the effort and engagement that students have devoted facilitates growth and change or even leads to departure from the institution.
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These theories of student success are based on research conducted primarily with students in the context of higher education in the United States that is a developed country with world-class higher education (Altbach, 2003; Mandernach, 2015). However, research on student engagement deploying the frameworks from these theories has been conducted in other education systems in both Western or non-Western countries (Choi & Rhee, 2014;
Coates, 2005; Hu, Ching, & Chao, 2012; Kahu, 2013). The research confirms that variables of student engagement that have been recognized in higher education as significant in predicting student success are relevant across higher education systems.
Hence, these theories are useful in examining the engagement of college students in
Indonesia.
Student Engagement and Educational Outcomes
Student engagement in the college setting has been studied by using the college impact model that attempts to examine the impact of individual as well as environmental factors on educational outcomes (Astin, 1993, 1999; Chickering & Gamson, 1987; Pace,
1984; Tinto, 1993; Pascarella, 2006; Pascarella & Terenzini, 2005). In college life, students and the institution routinely interact and create a college environment in a substantial way that, in turn, shapes students’ experiences. These experiences mostly originate from conditions or events that the institution facilitates to some extent for student learning and growth (Pace, 1984). In response to these conditions and events, students exert some degrees of effort to engage themselves in what the institution provides in terms of college experiences. The extent of their engagement is reflected in the psychological and physical efforts they invest in the college experiences (Astin, 1993, 1999). Kuh (2003)
60 revealed that about one out of five college first-year and senior students had no preparation when attending the class and reported that the institution less emphasized studying and allocating time on academic activities. In turn, the quality (effort) and quantity (amount) of the engagement by the student are a set of factors that will determine outcomes.
In the college setting, student engagement has been found as a critical factor that leads to positive educational outcomes including persistence, skill, commitment, academic performance, and completion. Berger and Milem (1999) reported that student engagement affected students’ persistence to graduation and their commitment to the institution they attended. Kuh (2003) found that engaged students reported higher gains including skills necessary for career advancement, problem-solving, critical thinking, independent learning, and speaking as well as writing competencies. The extent of engagement was also reported of positively affecting academic performance and persistence (Carini et al., 2006;
Kuh et al., 2008; Pascarella & Terenzini, 2005; Pike & Kuh, 2005). In addition, research studies have often revealed correlations between student engagement and educational outcomes including student satisfaction (Kuh & Vesper, 1997), improved GPA (Astin,
1993; Carini et al., 2006; Pike, Schroeder, & Berry, 1997), general abilities and critical thinking (Kuh, 2003; Kuh & Vesper, 1997; Pike, Kuh, & Gonyea, 2003), persistence
(Astin, 1999; Bean, 2005; Pascarella & Terenzini, 2005; Tinto, 1993). The studies also show that as times and efforts are focused on various educationally purposeful activities both inside and outside the classroom, students can learn more (Hu & Kuh, 2002; Kuh,
2003). The literature on student engagement also describes individual characteristics that are correlated with engagement.
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Student Engagement and Individual Factors
Student engagement literature identifies a relationship between student engagement and individual factors (Pascarella, 2006; Pascarella & Terenzini, 2005). Class level, gender major, working, and parental education are some students’ background characteristics that affect the behavior of their involvement in educational activities on campus. Female, senior, not working, Science, Technology, Engineering, and Mathematics (STEM) program, and continuing-generation students were often found of actively participating in the classroom, taking more classes, interacting with peers and faculty more often, studying more, and allocating more time for class preparation (Conway et al., 2011; Kinzie et al.,
2007; Kuh, 2003; Kuh, Kinzie, Buckley, et al., 2007; Pike & Kuh, 2005; Salamonson,
Andrew, & Everett, 2009; Webber, Krylow, & Zhang, 2013).
Academic Level. First-year and senior undergraduates display differences in terms of their level of engagement and college activities that matter to their success. In Webber,
Krylow, and Zhang’s (2013) study, compared to freshmen, seniors reported that they read more books on their own and wrote more papers, but spent fewer hours preparing for class.
Based on the results of the current NSSE (2019) administration, senior undergraduates were more engaged in higher-order learning, reflective and integrative learning, use of learning strategies, diverse discussions, interaction with faculty, effective teaching practices, and quality of interactions. Their first-year peers scored higher in collaborative learning activities and support environment.
In regards to the impact of engagement on student success in college, Webber and colleagues (2013) revealed that with faculty inside and outside of the classroom,
62 relationships with students, faculty, and staff, and institutional emphasis on support and interaction provided a substantial impact on first-year students’ college grades. For seniors, interactions with faculty, staff, and other students, engagement with diverse others, and participation in community service activities significantly impacted their academic achievement. Carini and colleagues (2006), also, found the effects of engagement on undergraduates’ academic performance varied by class level. The higher engagement in preparing more drafts of an assignment prior its submission, coming to class with sufficient preparation, receiving timely feedback from faculty, working harder to meet faculty’s expectations, having discussion on coursework with either peers or faculty outside of the classroom, writing shorter papers, and receiving academic support from the institution led to students’ increasing academic scores of first-year students. However, exposure to diversity imposed negative impact. The forms of engagement that mattered to seniors’ academic achievement were working in a group on an assignment or project in class, applying what has been learned from different courses to class discussion or assignment, receiving academic advising, and institutional support to diversity experiences as well as involvement in campus activities.
Different levels of engagement and its impact on the collegiate accomplishment of first-year and senior undergraduates are somewhat related to the range of experiences, the curriculum, and the out-of-class experiences to which both student groups are exposed
(Pascarella & Terenzini, 1991; Pike & Kuh, 2005). With their more years in college, senior students are more likely to have a wide range of experiences inside and outside of the classroom. In terms of curriculum, while coursework for freshmen puts more emphasis on
63 general education, seniors take classes with more focus on the major. Regarding the exposure to out-of-class experiences, first-year students tend to participate in formal extracurricular activities on campus while their senior peers have more opportunities for study abroad, internships, research activities, and others.
Gender. NSSE (2016) reported that in the survey on student engagement administered in the United States in 2015, in regard to gender, highly more female students participated in the survey than their male counterparts, 65 % and 35 % respectively. This finding is similar to a student by Kinzie and colleagues (2007) attempting to study relationship between gender and student engagement. In this study, over 470,000 freshmen and seniors who took NSSE in 2005 and 2006 were randomly selected for study participation. The sample demographics showed 36 % of the participants were male while
64 % were female. This study, also, revealed some intriguing distinctions of engagement patterns between male and female students at institutions in the United States higher education. male students reported that they spent more of their time on non-academic activities and events such as physical activities, relaxing, co-curricular events, and socializing. On the contrary, female students allocated more of their time to academic activities such as preparing class presentations, reading materials, and writing assigned papers. Furthermore, women exceed men in academic performance during their first year of higher education (DeBard & Sacks, 2010; Kinzie et al., 2007).
The study by Kinzie and colleagues (2007) revealed that when examining gender distinctions in engagement using five benchmarks of NSSE, male and female students exhibited differences (Kinzie et al., 2007). Compared to male students, female students
64 focused more of their time and effort to meet institutional expectations for academic achievement. Female students, also, more actively participated in the classroom and collaborative learning with their peers inside and outside the classroom. In regard to campus environment, female students reported that they received sufficient supports from their institutions. Both male and female students were, to some extent, equally engaged in student-faculty interactions and experiences with diversity. Hu and Kuh (2002) also found that female undergraduates are more academically engaged, and spent more time preparing for class. However, one also needs to consider the area of study being examined as some majors, such as nursing, remain female dominant and male students are underrepresented, and other areas of study are male dominant, such as mathematics and engineering
(Lackland & De Lisi, 2001).
Academic major. Academic major has been reported affecting student engagement of undergraduates (Brint et al., 2008; Lichtenstein, McCormick, Sheppard, & Puma, 2010).
Brint, Cantwell, and Henneman (2008) asserted that in general there are two major cultures of undergraduate engagement in college: STEM majors and non-STEM majors. While
STEM majors more focus on the enhancement of quantitative skills and use of collaborative study, non-STEM majors including the humanities, arts, and social sciences put emphasis on participation, interaction, interest, and exploration of ideas.
Using data from a single institution, Veenstra, Dey, and Herrin (2008) found that quantitative skills and related learning strategies are significant to only students majoring in engineering and social engagement is substantially critical for those studying in all majors except engineering. Hurtado, Newman, Tran, and Chang (2010) also revealed
65 students were more likely to participate in major-related activities and their participation positively influenced their success in college. Astin (1993) suggested that compared to other majors, engineering major had more significant impact on college experience and learning outcomes. Engineering major was positively linked with learning and analytical as well as job-related skills. However, negative correlations were found between engineering major and student satisfaction on overall college experience and diversity exposure.
By analyzing the data of NSSE, Lichtenstein and colleagues (2010) found that compared to peers in other majors, undergraduates majoring in engineering had significantly different degrees of engagement in six of the eight engagement scales under study. Engineering students scored significantly higher for gains in practical competence and higher-order thinking but reported the lowest means for gains in general in education, integrative learning, reflective learning, and gains in personal and social development. For the scales of support for student success and overall satisfaction, no significant difference was found. The researchers, also, revealed that engineering students were more likely to devote greater amounts of time to prepare for class and they spent significantly fewer hours working off-campus. A study by Ohland and colleagues (2008) revealed similar findings as Lichtenstein et al. (2010). The researchers found that while undergraduates studying engineering were engaged in college at a similar degree as their peers in other majors, engineering students indicated a higher likelihood to score higher in practical competence and lower in personal and social development.
Working. Studies have revealed that working off-campus is associated with lower gains of educational outcomes. Astin (1993) and Pascarella and Terenzini (2005) found
66 that this activity had a negative effect on persistence and degree completion. A study by
Kuh, Kinzie, Cruce, Shoup, and Gonyea (2007) shown that students who worked off- campus gained lower grades in their first year in college, with which they became more likely to leave college. These students tend to perform poorly academically in college since they allocated less time and effort to academic activities including class preparation, studying, class attendance, and homework accomplishment (Dietsche, 1990; Pascarella &
Terenzini, 2005).
Working par-time, regardless of the place of work, has been reported to be correlated with student success in college. In a study by Salamonson, Andrew, and Everett
(2009), when examining academic engagement of undergraduates, working part-time was found to significantly predict students’ learning gain. These non-academic activities and commitments prohibited them from participating in certain educationally purposeful activities and fully benefitting from college experiences. Furthermore, working part-time off-campus was found to have similar patterns as those that were associated with working full-time, which indicated a most profound negative effect on persistence and degree completion (Astin, 1993).
First-generation status. Continuing-generation students are those with either parent who had earned a college degree (Conway et al., 2011). Hence, students whose parents had not graduated from college are considered first-generation. In NSSE administration in 2015, over 40 % of the participants were first-generation students neither parent having completed an undergraduate degree (NSSE, 2016). Pike and Kuh (2005) found that first-generation students were less likely to be connected to college and engaged
67 in educationally purposeful activities than other students. Their lower engagement occurred since they were not well informed of the importance of engagement for their success in college. Since first-generation students are the first in their family who attends college, they may not have the guidance, role model, or experience in a higher education setting.
Students who have one or both parents with a completed baccalaureate degree are more likely to attend college. Pascarella and Terenzini (2005) reported that these students were twice as likely to attend college than their first-generation peers. Compared to first- generation students, students who had parents with education higher than an undergraduate degree were five times more likely to persist in college and then graduate. It is because they typically have their parents to guide their educational planning and provide necessary supports that, in turn, lead them to degree completion (Pascarella & Terenzini, 2005).
Pascarella and Terenzini (2005) further stated that “postsecondary education offers an intergenerational legacy in children’s knowledge acquisition” (p. 590). According to Kuh and colleagues (2008), parent educational attainment “is an important variable for predicting college predisposition among all low socioeconomic status students” (p. 20).
Conway and colleagues (2011) reported that first-generation status was associated with lower degrees of engagement. Moreover, Pascarella, Pierson, Wolniak, and Terenzini
(2004) revealed that the impact of engagement on undergraduates’ academic achievement was embedded within their parents’ educational attainment. In comparison with students whose parents graduated from college, a variety of engagement forms were found to have more substantial effects on the achievement of first-generation students. For instance, first-
68 generation students significantly benefitted from their involvement in co-curricular activities for critical thinking, higher-order cognitive task, degree plans, and academic success. However, the impact of co-curricular involvement on the same outcomes of their continuing-generation counterparts was either insignificant, smaller positive, or significantly negative.
Student Engagement and College Grades
Grade Point Average (GPA)
In studies examining the association between engagement and academic achievement, GPA is often used as an indicator of academic gain (e.g., Carini et al.,
2006; Fuller et al., 2011; Gordon et al., 2008; Melius, 2011). Not only on engagement, but also in many other research topics with the focus on postsecondary education, GPA is commonly chosen to serve as a measure of students’ learning outcome (e.g., Baker et al.,
2016; Burbidge, Horton, & Murray, 2018; Lepp, Barkley, & Karpinski, 2015; Parker,
Kilgo, Sheets, & Pascarella, 2016). GPA is considerably appealing due to accessibility and affordability in measuring academic achievement (Bacon & Bean, 2006). Also, it is able to account for academic achievement across courses and periods (Soh, 2010).
In spite of the popularity in research, the use of GPA to measure academic achievement has been questioned for its validity and reliability. Grade inflation that threatens the accuracy of GPA is the likelihood to provide higher grades for similar essential performance at a different period in time or at different degrees of study
(Johnson, 1997). The problem due to grade inflation occurs as it decreases comparability between grades that afterward constitute GPA (Johnson, 1997). Grade inflation, also, 69 may result in ceiling effects in which grades are concentrated close to the upper limit of the grading range (Poropat, 2009). Furthermore, ceiling effects possibly produce disrupted rank-ordering, range limitation, and non-normal distributions (Poropat, 2009).
GPA remains worth using because its internal reliability is adequate when the
GPA is treated as a scale comprising individual grades as the component items. Bacon and Bean (2006) reported that the average intraclass correlation coefficient for college
GPA was .94. The problematic reliability of GPA is more likely to impact the measure’s stability and its correlations with other measures or variables. However, studies reported significant correlations between secondary and postsecondary level GPAs that show this measure is reliable over time (Al-Hattami, 2014; Kobrin, Patterson, Shaw, Mattern, &
Barbuti, 2008; Schuler, Funke, & Baron-Boldt, 1990). GPA is, also, associated with other variables such as persistence (Braunstein & McGrath, 1997; Pascarella & Terenzini,
2005; Reason, 2003), graduation (Gershenfeld, Hood, & Zhan, 2016), enrollment in graduate programs (Mullen, Goyette, & Soares, 2003), and satisfaction with college experience (Grayson, 2004). The correlations of GPA with other variables exhibit criterion validity. Therefore, despite problems surrounding GPA, this measure remains useful to indicate college students’ academic achievement and to be used for research purposes.
If a factual GPA can serve as a useful measure of academic gain, does self- reported GPA perform similarly? If students report their college grades accurately, self- reported GPA can function identically to the actual one. However, as other self-reported responses, self-reported GPA is subject to a flaw of inaccuracy in reporting reality
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(Porter, 2011). Porter (2011) in his study identified the substantial reporting error for self- reported GPA. Nevertheless, other studies (Cassady, 2001; Kuncel, Credé, & Thomas,
2005) reported the considerable correlations between self-reported and actual GPAs.
Cassady (2001) examined the accuracy of students’ self-reported SAT scores and GPA and revealed that self-reported GPA was significantly and highly correlated with the actual GPA (.97). Kuncel and colleagues (2005) undertook a large scale meta-analysis study to investigate the accuracy of self-reported SAT scores, class ranks, and GPAs from 60,926 students enrolled at secondary and post-secondary levels of education. They found the average correlation of .84 between self-reported and actual GPAs, with the correlations for college students were higher than their high-school counterparts, .90 and
.82 respectively. Nevertheless, the correlations were moderated by students’ abilities.
College students with low ability were identified less likely to report their GPA accurately (Kuncel et al., 2005).
Ideally, research relies on actual instead of self-reported GPA. However, sometimes certain barriers, such as privacy issues and administrative regulation, conceivably occur that limit researchers from access to official records of student GPA.
With the limited access to actual GPA, the use of a self-reported GPA is appropriate but with caution (Kuncel et al., 2005; Poropat, 2009). Compared to other self-reported factual measures, such as SAT and class ranks, self-reported GPA demonstrates higher accuracy
(Cassady, 2001; Kuncel et al., 2005). Most college students check their GPAs more frequently than other measures and therefore they are likely to report GPA with better accuracy. Moreover, a self-reported GPA is based on factual data that is subject to
71 verification (Cassady, 2001). The verification can be performed for self-reported GPA by comparing to external data sources, such as official records, managed by the institution.
Anonymity is another factor that lessens the presence of social desirability bias and, in turn, promotes the accuracy of self-reported GPA (Shepperd, 1993).
Student Engagement and GPA
The college grades have been perceived as the best predictors of student success of undergraduates indicated by their statistically significant and often the large impact on student persistence, attainment, graduation, and even enrollment in graduate education
(Pascarella & Terenzini, 2005). Moreover, Astin (1993) asserted that regardless of their limitations, the college grades or GPA perform well of reflecting student learning and gain during the years of college. Therefore, grades or GPA have been widely accepted and then used to measure the academic performance of college students (Graunke &
Woosley, 2005; Handelsman, Briggs, Sullivan, & Towler, 2005).
It has been extensively studied that since its early emergence, student engagement has been reported to be positively associated with academic performance reflected on grades (Astin, 1999; Chesley, 2007; DeBerard, Spielmans, & Julka, 2004; Kuh, 2005;
Kuh, Kinzie, Buckley, et al., 2007). Astin (1977), in his early study, asserted that student involvement was found to positively affect undergraduates’ learning. Later, Astin (1993) conducted a study with larger participants built upon his earlier study. This study was longitudinal with more than 25,000 participant undergraduates enrolled at over 200 higher education institutions. The study participants were first involved in the study as they were in 1985 and then as they were senior in 1989. The data of this study were
72 collected from surveys, admission, retention, academic performance, and graduation test scores. From this study, Astin (1993) revealed that multiple dimensions of student involvement positively affected student development and learning.
Hugh and Pace (2003) studied the effect of student engagement towards undergraduate withdrawal. In this study, more than 150 freshman students at a single public institution participated in this study. The NSSE survey was administered and the responses from participant students were linked with their academic records. In the following year upon the survey administration, some of the participants were reported of leaving college. The study found that one of the major factors that contributed to students’ dropouts was grades. These dropout students reported that their grades were
“C” or even lower and were less engaged. Their counterparts who remained in college reported that they had higher grades and engagement in educationally purposeful activities.
Carini, Kuh, and Klein (2006) sought to examine the correlation between student engagement and academic performance. For data collection, this study utilized the survey on student engagement, NSSE, and academic record. Over 1,000 freshman and senior undergraduates at 14 colleges and universities participated in the study. These were four-year institutions and the privates were dominant. Most of the participant students were full-time and racially White. More than half were female and few were from racial minority groups. In the study, while student engagement was indicated by the five benchmarks of NSSE, academic performance was measured by GPA, Graduate
Record Exam (GRE) scores, and RAND scores. RAND was a set of tests that focused on
73 cognitive and performance areas with a duration of 90 minutes to complete each test. All these three academic measures were standardized for analytical purposes. Meanwhile, to measure student engagement, the study used NSSE which promoted five benchmarks of student engagement: academic challenge, active and collaborative learning, student- faculty interaction, enriching educational experiences, and supportive campus environment. The results of the study revealed that GPA had a positive correlation with many forms of student engagement with partial correlation ranging from .07 to .13. Also, this study found that self-reported learning gains were positively correlated with GPA
(Carini et al., 2006). Freshman students were reported of benefitting most from writing short papers, getting prepared when coming to class, making effort to meet academic expectations by faculty, and relationships with faculty as well as administrative staff.
Meanwhile, the largest contribution for senior students came from working on group projects with peers, receiving academic advising, and integrating ideas from other classes.
Kuh, Kinzie, Cruce, Shoup, and Gonyea’s (2007) study examined the impact of engagement on college grades of students controlling their background and pre-college experiences. This study involved thousands of undergraduates at multiple four-year institutions including Predominantly White Institutions (PWIs), Historically Black
Colleges and Universities (HBCUs) and Hispanic Serving Institutions (HSIs). The necessary data were gathered from the admission test score, financial aid record, the survey of NSSE, and academic record. In general, the study found that female students had higher grades than males, White students reported higher grades than others from
74 racial minority groups, and grades largely vary by major fields but it did occur by institutional types. The results from the data analysis performed by using regression measurements showed that the contribution of student engagement to college grades was statistically significant and positive. The study found that students with lower grades were those whose time was more used to work off-campus, actively participate in co- curricular activities, and relax or socialize.
The researchers found working part-time discouraged students to devote sufficient time and effort to attending class, studying, as well as doing homework. Meanwhile, students who had higher grades were those who spent more time studying every week.
These findings were in line with the principle of student engagement that becoming engaged by exclusively allocating time and effort in educationally purposeful activities was associated with better educational outcomes. According to this study (Kuh, Kinzie,
Cruce, et al., 2007), engagement could benefit all students with diverse characteristics and pre-college experiences. Furthermore, the impact of student engagement on academic performance was larger for historically underserved students. It yields the evidence that students’ individual background determines the degrees of student engagement contribution to learning.
Gordon, Ludlum, and Hoey (2008) examined the link between student engagement and certain collegiate outcomes including freshman persistence, academic performance, degree completion, and employment upon graduation. The researchers chose the NSSE benchmarks and GPA to measure student engagement and academic performance respectively. In this study, the necessary data were obtained from about
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1,800 and senior students at a major research institution. Regression analysis was conducted to identify the predictive power of the benchmarks on grades. The results of the study revealed student engagement had a positive impact, although modest, on grades and other collegiate outcomes examined. For academic performance, Freshmen benefitted most from level of academic challenge, active and collaborative learning, and enriching educational experiences, while supportive campus environment and student- faculty interaction were most highly beneficial for seniors.
Melius (2011) also conducted a study to examine the correlation between students’ background characteristics, college environment, engagement in effective educational practices, and college grades. 670 freshmen and seniors attending a baccalaureate degree-granting institution participated in this study. The majority of participant students were female and identified as African American. The data were obtained from NSSE responses and cumulative GPA. This study utilized statistical analyses of t-tests and analysis of variances to identify whether students’ score differences in their academic record and engagement reflected in the five benchmarks of
NSSE were significant. Also, multiple regression analysis was used to examine the extent to which student engagement impacted the academic outcome of GPA. Melius (2011) found that students significantly scored lower than their seniors in active collaborative learning and student-faculty interaction. Compared to female students, their male peers were reported to interact with their faculty inside and outside the classroom more often and more actively participate in co-curricular activities. Regarding residence, students living on campus were found to be more engaged in supplementary learning activities
76 than commuter students. The study surprisingly revealed that, when the benchmarks were individually tested, none of the individual benchmarks significantly contribute to students’ college grades. However, as the five benchmarks were analyzed as a single construct of engagement, the increased engagement was associated with higher GPA
(Melius, 2011).
Fuller, Wilson, and Tobin (2011) attempted to examine whether student engagement could be the best predictor of academic performance in a longitudinal and cross-sectional sample of undergraduates at a single large four-year institution. In the longitudinal examination, the study traced students who completed the NSSE survey in their first and senior years of college and it yielded 127 participant students. More than
4,800 freshman and senior students’ responses across seven years were used in the cross- sectional analysis. The college GPA was utilized to measure academic performance while the NSSE benchmark scores to indicate student engagement. In this study, the benchmarks of NSSE were included except enriching educational experiences due to major changes in NSSE in 2003. The study utilized regression analysis to examine the predictive relationships between the benchmarks of NSSE and GPA by controlling pre- college academic achievements. The results from the cross-sectional analysis revealed that the benchmarks of student engagement significantly predicting the college grades were the level of academic challenge for freshmen and active while active collaborative learning for seniors. These findings yielded evidence that students’ behaviors toward academic performance were changed in different stages of their college years.
Alternatively, the results from the longitudinal analysis revealed that students were more
77 engaged when they were senior than in their indicating growth during their time in college. However, none of benchmarks under study was found as a significant predictor of grades. The researchers stated that the small sample size was responsible for limited ability of the longitudinal examination in this study to spot modest effects associated with predictor factors.
The vast majority of the abovementioned studies on student engagement were undertaken in the context of higher education in the United States. This fact indeed poses a question of whether student engagement performs effectively in bolstering students’ academic performance in other higher education systems. Studies on student engagement in the other nations consistently reported engagement is of substantial significance to student success in college (Khaira, 2016; Neves, 2018; Sweeney, 2016; Yashuang, 2013).
Khaira (2016) studying engagement of Canadian undergraduates in the nursing program revealed that engagement was correlated with GPA. In her study, she utilized five benchmarks of NSSE, including level of academic challenge, active and collaborative learning, student-faculty interaction, enriching educational experiences, and supportive campus environment. Khaira (2016) found the level of academic challenge and enriching educational experiences showed the strongest positive correlation with college grades of
Canadian undergraduates.
Yashuang (2013) analyzed the 2010 dataset of China College Student Survey
(CCSS) to examine Chinese undergraduate students’ engagement and its impact on their learning achievement. The study revealed that engaged students were found of having a higher likelihood to gain higher academic grades than those with lower engagement. Of
78 the engagement indicators in the study, peer-interactions had the most substantial impact on GPA. In Irish postsecondary education, Sweeney (2016) found that those who were more likely to express positive satisfaction and achieve higher college grades were students with higher engagement in student-faculty interactions, interactions with diverse peers, and supportive environment. This study placed students’ relationships with others
(faculty, staff, and peers) and institutional commitment to provide services and activities to support student learning as the major contributors to their success in college. Neves
(2018) asserted that student engagement and academic gains were positively correlated.
Four engagement indicators showed significant correlations with students’ grades: student-staff interactions, partnership with staff, research and inquiry, and reflecting and connecting (Neves, 2018). This study asserted that the staff was the most critical factor in engaging college students in the United Kingdom.
The studies by Astin (1977, 1993), Hugh and Pace (2003), Carini et al. (2006),
Gordon et al. (2008), Fuller et al. (2011), Melius (2011), Yashuang (2013), Khaira
(2016), Sweeney (2016), and Neves (2018) serve as a solid basis showing the association between student engagement and academic performance, particularly for undergraduates.
These studies provide empirical evidence that regardless of students’ high school academic achievement and pre-college experiences, involvement in educational activities benefits them to enhance their college grades, which, in turn, leads them to persistence, degree completion, employment, and higher study.
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An instrument to Measure Student Engagement
For over seven decades, student engagement has been in the literature and researchers have attempted to develop instruments to identify as well as to measure a variety of educational practices that reflect student involvement in educationally purposeful activities (Kuh, 2009). Today, the most influential instrument to measure student engagement in the college setting is the National Survey of Student Engagement
(NSSE). Since its first launch in the early 2000s, NSSE has caught significant attention from researchers, administrators, and stakeholders in higher education not only in the
United States where it originated but also in other countries (Zepke, 2013). Hence, NSSE has spread its influence and became a reference of later instruments on student engagement around the world such as Canada, Australia, New Zealand, China, Ireland, the United Kingdom and recently Indonesia (McCormick et al., 2013; USAID, 2014).
The National Survey of Student Engagement
The NSSE survey is currently the instrument that has been widely administered as well as utilized to capture certain practices or activities indicating student engagement in college. This instrument was initially developed in the late 1990s by the Center for
Postsecondary Research at Indiana University Bloomington (IUB) to examine the extent of time and effort undergraduates devote to be engaged in educationally purposeful activities and their educational gains from college experiences (Kuh, 2003). The administration and use of NSSE are focused in the United States higher education, but it has influenced the development of later instruments on student engagement in other places. 80
The emergence of NSSE in the United States was partially resulted from accumulating discontent with the dominance of rankings to measure college quality.
Since the beginning of the 1980s, lists of college rankings in the United States have been annually published by popular media with the aim to identify as well as separate excellent institutions from the rest. College rankings have become increasingly popular among institutions as well as the general public due to partially the implied endorsement from universities and colleges that excessively exploit their standing in the rankings for promotional and recruitment purposes (Hazelkorn, 2015; Kuh, 2009). The prestige and revenue advantages that college rankings offer have motivated institutions to devote more resources to preserve or pursue higher ranking. However, college rankings received a variety of objections in terms of their philosophical and methodological aspects (Graham
& Thompson, 2001; Hazelkorn, 2015; Thacker, 2008). One of the persistent criticisms of college rankings is their heavy reliance on reputation and ranking criteria that exclude the substantial aspects of teaching and learning (Hazelkorn, 2015). Hence, until the early
2000s, college rankings did not contribute enough to improving the educational outcomes of college students in the United States. For instance, in this period, college completion rates remained stagnant at below 60 % (McCormick et al., 2013).
In the context mentioned above, the Pew Charitable Trusts carried out an initiative to financially support the development and implementation of an instrument,
NSSE, with the focus on process indicators associated with best practices in college
(Kuh, 2009). This instrument was designed to be primarily focused on student engagement with the inclusion of behavioral as well as environmental aspects that have
81 been empirically found to be associated with desired educational outcomes in the college setting. Kuh (2003), as NSSE’s founding director, described that the NSSE survey consisted of a set of constructs to measure the extent of time and effort that students allocate to educational activities inside and outside the classroom. Further, he highlighted that student the concept of student engagement entailed in NSSE was a critical factor to enhance student success in college and a better indicator of college quality than the rankings (Kuh, 2003, 2009). NSSE is designed for diagnosis as well as improvement functions and expected to reframe the general public understanding of educational quality in college (Kuh, 2009).
The primary content of NSSE reflects behavioral and environmental factors that are highly associated with desired educational outcomes in the college setting (Kuh,
2003). These outcomes include persistence, degree completion, academic achievement, and satisfaction. The NSSE survey requires students to reflect on what they have invested, in terms of time and effort, in their college experiences. Then, the survey attempts to capture behavioral and environmental aspects of their experiences in college, which represents a set of different dimensions of student engagement.
NSSE does not function as a direct assessment tool for student learning, but the results of NSSE can show what areas that an institution performs in enabling students to enhance their learning and what aspects of students’ educational experiences that need improvement (Kuh, 2003). From NSSE implementations, this survey has demonstrated that it can reliably assess student engagement across a large number of universities and colleges in the United States and the results can be immediately available to be used by
82 faculty, administrators, students, and others showing interest in student engagement
(Kuh, 2009). NSSE results have been used in various ways, for instance facilitating a cohort of experience development intended for groups of students, monitoring academic standards, promoting student learning and growth, accountability and transparency, institutional improvement, and enhancing student persistence as well as degree completion (NSSE, 2010), In particular, Banta, Pike, and Hansen (2009) mentioned that
NSSE had four main purposes: accreditation, accountability, strategic planning, and program evaluation.
The NSSE administration is intended for freshman and senior students across participating institutions in the United States. Institutions are encouraged to participate in
NSSE every three years and therefore, they can track engagement of the same students in their first and senior years. The survey is administered at participating institutions in the spring term to enable participant students to have sufficient experiences that the questions of NSSE attempt to measure (NSSE, 2019). NSSE is composed of questions focusing on collecting five types of information from undergraduates, including student background characteristics, educational as well as personal growth, opinions on institutional environment, institutional expectations and emphasis on learning, and participation in a variety of educationally purposeful activities.
Before 2013, to measure student engagement, NSSE used over 40 questions that were grouped into a cluster of similar activities indicating five benchmark scales of effective educational practices in college. The use of benchmarks in NSSE was aimed to enable result comparisons among institutions or even higher education systems (Kuh,
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2000). The first benchmark was the level of academic challenge that was composed of a set of questions focusing on the amount of time and energy that students devoted to class preparation, reading course materials, writing assigned papers, and institutional expectations for student performance. The benchmark of active and collaborative learning used certain questions to measure students’ participation in the class, academic group work with their peers in and out of the classroom, tutoring activities, and educational discussions with others outside of class.
The next benchmark was student-faculty interaction. It was driven from the questions that regarded the frequency and extent of students’ interactions with faculty through discussing with faculty on such topics as their classes, subjects, grades, and career plans, receiving feedback from faculty, and working with faculty on a research project or other non-academic activities. The benchmark of enriching educational experiences consisted of certain questions to examine students’ interaction with their peers with diverse background characteristics, use of technology for learning, and participation in supplemental learning activities such as internship, independent study, study abroad, and community services. The last benchmark of NSSE was supportive campus environment that utilized the questions with the focus on capturing students’ perspectives on supports that an institution and its agents provided to lead them to be academically and socially successful in college
After 13 years of implementation, NSSE was updated in 2013 as a response to the empirical evidence showing that the initial survey with its five benchmark scales did not perform optimally to identify student engagement as well as the needs coming from
84 institutions (McCormick et al., 2013). In the updated NSSE, the benchmarks become themes that are composed of ten engagement indicators: higher-order learning, reflective and integrative learning, quantitative reasoning, collaborative learning, effective teaching practices, and supportive environment (NSSE, 2013). The benchmark of enriching educational experiences in the previous survey was excluded from the main dimensions of engagement to be a measure of high impact practices (HIPs) identifying students’ involvement in supplemental learning activities such as learning community, service- learning, research with faculty, internship, study abroad, and culminating senior experiences (NSSE, 2013). Each theme in the current NSSE consists of two to four indicators (NSSE, 2018). Of 47 core question items of engagement in NSSE, every indicator is composed of three to eight related questions. The survey items employ a four- point Likert scale (1 = never; 4 = very often or 1 = very little; 4 = very much). Although updates were undertaken, NSSE’s initial underpinning design remains that perceives engagement as educational practices involving students, faculty, and institutions that can be identified and quantified.
NSSE has been employed in many studies that build upon the current literature on student engagement (e.g. Carini et al., 2006; Fuller et al., 2011; Gordon et al., 2008;
Hughes & Pace, 2003; Laird et al., 2008; Kuh, 2001b; Kuh et al., 2008; Melius, 2011;
Pascarella, Seifert, & Blaich, 2010; Prokess & McDaniel, 2011). The updated NSSE was used as a framework for the development of an instrument on student engagement in
Indonesia, namely the Indonesian Survey of Student Learning Activities (ISSLA). In recent years, this survey has been administered across higher education institutions in
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Indonesia by the government and the participating institutions to understand students’ college experiences and engagement (USAID, 2014). Unlike in the United States and other countries with more established higher education systems, the government and institutions in Indonesia have not yet utilized the results of ISSLA for accreditation, accountability, strategic planning, and program assessment purposes. It is supposedly because student engagement and its survey remain relatively new to Indonesian higher education.
Criticisms of the National Survey of Student Engagement
Although NSSE currently enjoys increasing popularity among students, faculty, institutions, and policymakers in the United States and draws great international attention, it receives criticisms complaining its function as a tool for student learning assessment as well as improvement. There is the opinion that institutions participating in
NSSE exploit the results of the survey to result the impression that they give substantial attention to the enhancement of their students’ college experience despite the mere effort or resource devoted (Kahu, 2013). Also, the decentralized system of higher education in the United States allows individual institutions, programs, and faculty to utilize the survey for self-improvement only if they show commitment to it (Kahu, Nelson, &
Picton, 2017).
Another complaint regards what NSSE attempts to measure. It has been criticized that NSSE was designed with the use of a traditional college model and hence, some question items of the survey may not be relevant for other institutional types (Zepke &
Leach, 2007). For instance, students in commuter colleges whose behaviors are greatly
86 different from their peers at traditional institutions. These students are more likely to spend increased hours to work and look after their dependents and therefore they have lesser opportunity to interact with their peers and faculty outside of the classroom. Other students who also experience irrelevance of the NSSE questions are those who are at the same time enrolled at multiple institutions. There is also another opinion that NSSE questions related to technology seem outdated (Kahu, 2013). The faculty’s response to online posts from students is probably considered as engaging as students’ physical interaction with the faculty.
In regard to what to measures, NSSE has been questioned whether the question items are understood and relevant in the same way by distinct student groups (Kahu,
2013). There is the potential that students from different racial or ethnic backgrounds probably understand the NSSE question items in different ways. NSSE was also criticized for its considerable emphasis on behaviors over learning (Jaschik, 2009; Kahu et al., 2017).
The last source of criticism of NSSE as a self-report survey is its validity. The responses to the questions of this survey heavily depend on students’ ability to recall their recent activities and experiences in college. Porter (2011) posed certain critiques toward
NSSE’s validity. First, what the NSSE items questioned tended to be greatly broad and were less supported by underlying theory. NSSE had a very wide content domain that any of its items could be a part of different areas including college quality, educational outcomes, and student engagement. Also, it lacked a supportive theory that specifically became the basis for the inclusion of certain items in the survey. Second, students
87 participating in NSSE could make mistakes while providing responses due to their lack of comprehension and retrieval skills of what the items questioned. The less accurate responses could lead to coding and reporting problems for events and behaviors that
NSSE attempted to measure. Third, NSSE potentially resulted in various strategies of estimation, which, in turn, can result reporting problems. For instance, while NSSE had five constructs comprising its five benchmarks to measure student engagement, the result of the survey administration at a single institution indicated three constructs
(Swerdzewski, Miller, & Mitchell, 2007) and even eight constructs at another institution
(LaNasa, Cabrera, & Transgrud, 2009). Last, NSSE was complained about its question ambiguity. Some questions in NSSE were ambiguous that were potentially misunderstood and misinterpreted. Also, the ambiguous questions would considerably contribute to problematic reporting.
The aforementioned sources of criticism of NSSE have been resolved by studies conducted by either the internal researchers of NSSE or other researchers. In regard to the critique that NSSE tended to measure behaviors instead of learning, it is necessary to undertake validation to ensure the data of NSSE are aligned with the data produced by other instruments and information sources. Since this critique received the attention of
NSSE, the internal researchers of NSSE have undertaken a self-study and the participating institutions have been encouraged to examine and explore their data.
Pascarella, Seifert, and Blaich (2010) undertook a study to compare NSSE and the
Wabash National Study of Liberal Arts Education. In general, while NSSE with its five benchmarks focuses on student engagement, the Wabash study aims at longitudinally
88 identifying substantial factors that contribute to the outcomes of liberal arts education.
The Wabash study, in its quantitative part, has a test to assess learning outcomes administered in the first and final years of college. The study by Pascarella and colleagues (2010) revealed that the outcomes of NSSE and the Wabash study were positively correlated. It provided the evidence showing that student engagement indicated by the NSSE benchmarks was associated with educations gains such as effective reasoning, problem-solving, critical thinking, moral character, and leadership. With this confirming evidence, NSSE could be considered valid (Pascarella et al., 2010).
Therefore, institutions can utilize NSSE to identify which factors that affect and which are not present in student engagement, which, in turn, enhances student learning.
To address the criticism regarding the validity of the NSSE survey from Porter
(2011), NSSE has provided more information on its website about the study process and results regarding the survey validity and reliability. NSSE acknowledges that the results from the survey remain general and broad and hence may need further examination, but the survey questions were extensively reported of receiving consistent and expected understanding from participant students. For instance, although NSSE used the terms faculty and instructor inconsistently, in its focus group sessions, students were more likely to express these terms interchangeably (Schmidt, 2009). Hence, this evidence over- argued Porter’s (2011) complain of the ambiguity of the survey questions.
Culture and Educational Activities
Ross and Chen (2015) highlighted that culture does not have the capacity to fully explain the complexity of students’ educational practices in the college setting. However, 89 culture substantially constitutes context by which student learning and educational environment are bounded (Altbach, 2016; Kahu, 2013). The concept of student engagement that underpins ISSLA is designed and intended for the United States higher education that is to some extent contextually different from tertiary education in
Indonesia. The aim of this study is not to examine the cultural aspects of Indonesian college students’ engagement. However, culture is a useful lens to describe findings of the study regarding potential differences of student engagement between Indonesia and the United States from which the idea of engagement originated. Culture is integrated in the study to yield sound explanation of the patterns or degrees of Indonesian undergraduates’ engagement indicated by the indicators (higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment) in ISSLA and the impact of engagement on their academic performance that probably differ from findings of the corresponding research in the context of the United States.
Hofstede’s concept of culture. According to Hofstede (2001), culture is the collective values, beliefs, and practices that make the group as well as its members distinct from another. Hofstede (2001) asserted that culture comprises five dimensions: individualism and collectivism, power distance, uncertainty avoidance, short-term and long-terms orientation, and masculinity and femininity. The five dimensions of culture by
Hofstede (2001) have been widely utilized to study the impact of national culture on the members’ beliefs, values, and behaviors.
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Hofstede (2001) asserted that the dimension of individualism and collectivism is associated with the extent to which individuals are supposed to be independent or a part of a community or group. Hofstede (2001) suggested that individual cultures, such as the
United States, put front individuals and their families’ interests over the interest of groups and more emphasizes independence, freedom, and competition. In the context of the
United States higher education, individualism is manifested in such educational practices as academic freedom, intellectual ownership, assertiveness, pioneering, and individuals’ freedom in the academic program and course selection (Shapiro, Farrelly, & Thomas,
2014). On the contrary, collectivist communities, including Indonesia, are comprised of such characteristics as societal harmony, collaboration, saving face, families and communities as a central role, and community over individual needs as well as benefits
(Koentjaraningrat, 1999; Mangundjaya, 2013).
In Indonesian higher education, collectivism can be found in, such as, learning with more emphasis on collaborative activities, college choice and enrollment that put families as a critical influencer, and curriculum as well as instructional practices with inclusion of morality and character building as the educational goals (Arai & Handayani,
2012; Fraser, Aldridge, & Soerjaningsih, 2010; Kusumawati, 2013; Meliono, 2011). The dimension of individualism and collectivism is useful to explain the findings of this study regarding Indonesian undergraduates’ engagement, particularly collaborative learning, discussion with diverse others, and quality of interactions. Also, the dimension can dismantle the influence of parental education on the impact of engagement on college students’ academic achievement in Indonesia.
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The other dimension of Hofstede’s (2001; Hofstede, Hofstede, & Minkov, 2005) concept of culture is power distance. Hofstede (2001) defined this dimension as the degrees of individuals with less authority within communities or organizations expect and accept unequal power distribution. The dimension of power distance can explain the findings regarding interactions of college students and faculty as well as campus staff in
Indonesian tertiary education. Achua and Lussier (2010) stated that in high power- distance societies, more and lesser powerful individuals are less likely to interact as equals. By contrast, within communities with low power-distance, interactions between individuals in higher and lower positions tend to occur in an equal way. In college, faculty members have been perceived as the authority of knowledge that turns a symbol of power in educational activities (Bourdieu, 1989). Unlike college in the United States where a casual conversation between faculty and students may happen (Cook-Sather &
Abbot, 2016), in Indonesian higher education, in which high-power distance is obvious, student-faculty interactions are most probably carried out formally to show respect for faculty (Naibaho & Adi, 2012).
In Indonesia, when meeting or interacting with faculty, students have to perform such norms as dressing up, bowing or kissing hands, using “sir” or “ma’am” or “prof”, no interrupting, and beginning their questions, suggestions, or complains by saying “if you do not mind”, “I am sorry”, or other similar expressions. These norms, also, apply to students’ interactions with campus staff (Saputra & Yuniawan, 2012). Saputra and
Yuniawan (2012) revealed that student-faculty interactions mostly take place inside the classroom and more focus on academic matters. Outside of the classroom, students
92 typically see faculty to gain approval for academic evaluation, study plan, or academic- related documents and to have a consultation on the thesis for seniors. The context of
Indonesian tertiary education where high power-distance is dominant may cause undergraduates to develop different behaviors of engagement than their counterparts in other higher education systems.
Hofstede (2001) described that the dimension of uncertainty is the extent to which individuals feel uncomfortable or even threatened towards ambiguous or unfamiliar situations. Hofstede, Hofstede, and Minkov (2005) found that on the one hand, some cultures, such as the United States, Russia, and South Korea, shown a relatively low degree of uncertainty avoidance that individuals had a higher tolerance for situations they perceived ambiguous or uncertain. On the other hand, some others, such as Switzerland and China, had a high level of uncertainty avoidance indicated the members of a culture who were more likely to be uncomfortable in unfamiliar situations. Indonesia falls into a culture with a high level of uncertainty avoidance (Mangundjaya, 2013). The transition from high school to college is a challenging process for Indonesian undergraduates (Zein,
2017). Finding college with a new environment, which is mostly different from high school, leads students to experience individual, social, and academic pressures that possibly result in certain behaviors of engagement.
In Indonesia, families and friends have been found as critical factors that ease students’ challenges not only in the transitioning process but also throughout the years of college (Hartono, 2012; Zein, 2017). Families, particularly parents, who have college experience are one of the major sources of information and support for students to enroll
93 and retain in college. Students’ reliance on family is reflected in family involvement in such practices as college or major decision making, living arrangements, and extracurricular activities (Hartono, 2012; Kusumawati, 2013). In addition to families, friends are the ones Indonesian undergraduates consult when they have questions or problems regarding college before and during college years. Moreover, friends are a critical consideration in students’ participation in college preparation and decision making in college or major choice or course selection (Kusumawati, 2013). Hence, it is common that students go to pre-college programs, enroll in universities, and take classes where their friends are. High uncertainty avoidance and substantial reliance on families and friends in dealing with unfamiliar situations in college may affect engagement of
Indonesian undergraduates, particularly in their interactions with peers and institutions.
The dimension of masculinity and femininity in Hoftsede’s (2001) concept of culture refers to the extent to which the roles between the genders are distributed.
Masculinity is associated with assertive, tough, and competitive personalities, while femininity includes modest, tender, and caring characteristics (Hofstede et al., 2005). In masculine cultures, gender roles are distinct and therefore, a gap between males and females’ values is present. On the contrary, the gender roles between males and females overlap in feminine cultures. Although gender equity has been increasingly accepted in
Indonesia, the country remains a masculine culture by having a clear cut of roles between the genders (Mangundjaya, 2013). The different social expectations for males and females in Indonesia may have impact on behaviors and degree of undergraduates’ participation in educational activities in college.
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The last dimension Hoftsede (2001) conceptualized is long-term and short-term orientation. Hofstede and colleagues (2005) described that long-term orientation emphasizes virtues focusing on the future, such values as determination and carefulness.
Unlike long-term orientation, short-term orientation fosters virtues regarding the past and present and hence is associated with the values of meeting a social obligation, respecting norm or tradition, and protecting “face”. Indonesia tends to have a time orientation with the focus on the past and present and hence its culture entails short-term orientation
(Mangundjaya, 2013). This culture to some extent influences educational practices in tertiary education. With a strong short-term orientation, students’ appreciation of their participation in educational activities depends on the benefits in the short run. For instance, in Indonesia, students view college grades as a critical factor that leads them to graduation and the desired job or career path (Zein, 2017). Hence, with grades as the major goal, students prefer learning strategies, interactions, services, and college activities that they perceive beneficial to enhance their grades (Kartika, 2008).
Summary
Although the above studies and literature have discussed various aspects of student engagement, a number of gaps in the current literature also indicate the significance of this study. First, a large amount of literature and studies regarding the relationship between student engagement and college grades used data of NSSE collected before 2013. With revisions and changes made in the updated survey of NSSE in 2013
(McCormick et al., 2013), from which the survey on student engagement in Indonesia was developed, the findings from the studies utilizing the initial version of the survey 95 may not accurately and sufficiently reflect the current engagement of undergraduates.
This study utilized ISSLA adapted from the updated NSSE to examine student engagement and its impact on academic grades for Indonesian undergraduates.
Second, many existing studies exploring student engagement were conducted in more established higher education systems such as the United States, Canada, Australia,
New Zealand, China, Ireland, and the United Kingdom (e.g., Astin, 1993; Carini et al.,
2006; Coates, 2010; Fuller et al., 2011; Gordon et al., 2008; Hong, 2010; Hugh & Pace,
2003; Kuh et al., 2008; Lowe et al., 2017; Maskell & Collins, 2017; Melius, 2011;
Nelson et al., 2012; SHu, 2011). Student engagement in the context of countries with developing higher education system like Indonesia remains understudied. Kahu (2013) asserted that the nature of student learning in college is indeed contextual. It means that the results from the previous studies on student engagement merely apply to the context where those studies were undertaken. Hence, it is critical to conduct more research on student engagement in other or different contexts from existing literature.
Finally, little is known about student engagement and academic grades in the context of Indonesian higher education. A substantial effect of a low GPA on dropouts among undergraduates in Indonesia is evident (Agustiani et al., 2016; Hari et al., 2018;
Imran et al., 2013; Logli, 2016; Saefuddin & Ratnaningsih, 2008). Student engagement can serve as an antidote to the problematic college grades and optimize the institutional impact on student academic success. Due to the significance of student engagement, the government developed ISSLA and firstly implemented this survey in 2013 (USAID,
2014). However, the impact of student engagement on college grades in Indonesian
96 higher education has been little explored. This study was sought to address this gap in the literature on student engagement and GPA in the Indonesian context.
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Chapter 3. Research Methodology
The chapter discusses an overview of the research methodology that the study utilized to examine the impact of student engagement on undergraduates’ academic performance in the context of Indonesian higher education. There are four sections that constitute this chapter, research questions, data source, data collection, variables, analytical methods, and limitations.
Research Questions
The purpose of this study was to understand the impact of student engagement on college grades of students in Indonesia. Thus, there were three research questions that guided the study t to achieve its goal:
1. What is the relationship between student engagement and GPA among Indonesian
undergraduates?
2. What is the relationship between student engagement and student background
characteristics (academic level, gender, major, working, and first-generation)
among Indonesian undergraduates?
3. Does student engagement have an impact on GPA among Indonesian
undergraduates after controlling for their background characteristics?
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Data Source
The study primarily used a single data source, which was the collected responses of Indonesian undergraduates participating in the Indonesian Survey of Student Learning
Activities (SSLA) in 2017. These students were enrolled at four different public universities across Indonesia. This survey provided information on student engagement comprised of the ten indicators: higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment. In addition, it asked high impact practices, learning outcomes, and student background characteristics. The items of high impact practices focused on students’ participation in complementary learning activities including service- learning, research with faculty, internship or fieldwork, and a culminating senior project.
The survey had the items to obtain information on students’ learning outcomes such as grades, satisfaction with general college experience, and perceived learning gains. Some questions were dedicated to identifying student background characteristics that potentially impact students’ learning gains or engagement such as academic level, residence, working hours, parents’ education, and academic major.
ISSLA is an adaptation of the NSSE in the United States that fits the context of
Indonesian higher education with the main targets of first-year and senior undergraduates.
The survey was designed to obtain information on the learning activities of undergraduates associated with their success in college (USAID, 2014). The concept of student engagement was introduced in Indonesia in late 2012 and then the instrument, 99
ISSLA, was developed and piloted in 2013. The development of ISSLA involved United
States Agency for International Development (USAID) through Higher Education
Leadership and Management (HELM) project, the Indonesian government represented by
Directorate General of Higher Education, the researchers from NSSE, the Australian
Council for Educational Research, and partner universities and colleges in Indonesia.
In the development stage of ISSLA, a set of steps were undertaken: (i) translating and adapting of the survey to the Indonesian context; (ii) selecting and confirming formal agreement with the pilot institutions; (iii) conducting trial focus group and training invigilators; (iv) implementing at the pilot institutions; (v) completing data entry and analysis; (vi) preparing psychometric and institutional reports; and, (vii) completing a summary of results and holding of a discussion forum with partner institutions (USAID,
2014).
ISSLA was named in the Indonesian language “Survei Aktivitas Pembelajaran
Mahasiswa Indonesia” or SAPMI. The team of survey developers determined the literal translation of “student engagement” because the term tended to have political rather than academic connotations in the Indonesian context. The term “aktivitas pembelajaran”
(student learning activities) was used instead as it had the closest equivalent meaning to what student engagement was defined in the United States. The ISSLA development attempted to retain the concept of student engagement developed by NSSE for comparability, but the questions and answers were adjusted to fit the context of
Indonesian higher education. A number of questions were eliminated since these were not relevant or inappropriate in Indonesia and some others were adapted by modifying or
100 adding different answer choices. Table 3.1 below lists an example of questions that were either eliminated or adapted and mentions the reasons for the changes.
Table 3. 1 Pilot ISSLA Question Items Eliminated or Adapted from NSSE
Elimination Question or Reason Adaptation Are you a part-time student? Eliminated The law requires undergraduates in Indonesia to be a full-time student Are you taking a double Eliminated The law does not allow undergraduates major? to take more than one major at the same institution Are you a distance student Eliminated Undergraduates in Indonesia should study on campus except for students at the Open University What is your sexual Eliminated The question is considered too sensitive orientation? in Indonesian culture What is your race/ethnicity Eliminated The question is considered too sensitive in Indonesian culture Are you a member of a Eliminated Indonesian higher education does not fraternity or sorority have fraternities or sororities What type of housing are Adapted The response choices are adapted to you living in? types of living arrangements in Indonesia What co-curricular Adapted The response choices are adapted to activities do you belong to? types of college organizations at higher education institutions in Indonesia
The other important steps in the ISSLA development were focus group discussion and cognitive interview intended for first-year and senior undergraduates at a public university. After filling out the ISSLA questionnaire, they participated in a discussion with the focus on the clarity of the questions and the consistency of the responses. The
ISSLA researchers (USAID, 2014) reported that the students did not have difficulties to understand the questions as well as terms thus, further definition or explanation was not
101 necessary. Their responses, also, showed that they did not differ from undergraduates in the United States in understanding the questions. For example, the student participants understood the four-point range of answers as a continuum from what would be generally considered high to low frequency. Another point of similarity was their understanding of such terms as ‘values’ and ‘ethics’.
As the initial ISSLA was completed, it was piloted in 2013 to examine whether the survey would prove a useful tool for Indonesian post-secondary education institutions to generate the data of student learning activities, which was useful to improve the quality of educational practices and institutional effectiveness. The ISSLA researchers (USAID,
2014) reported that the survey piloting involved three institutions: a public university, a private university, and a polytechnic. The survey was administered using a paper-and- pencil mode to freshmen and seniors at each institution. The pilot study of ISSLA yielded different degrees of student engagement across the institutions (USAID, 2014).
ISSLA development and early implementation were organized and funded by the
HELM/USAID project. After the HELM/ USAID project ended, ISSLA is currently organized by a center at Gadjah Mada University, a public research institution, and has been administered to more state institutions across Indonesia since 2016. This survey is administered in paper and online forms. The participation in the ISSLA survey charges an institution about IDR 20 million (USD 1,800). The participating institution provides the center student contact information, and the center, then, contacts enrolled students at the participating institution to seek their voluntary participation in the ISSLA survey. The
102 center undertakes an administration of the survey and analysis of collected responses.
Last, the participating institution receives a data file and a suite of reports.
Validity and Reliability
Like NSSE in the United States, ISSLA in Indonesia asks undergraduates to self- report the time and effort they devote to educationally purposeful activities inside and outside class as well as institutional supports to promote engagement they receive (Kuh,
2003; USAID, 2014). Self-report surveys have been subject to criticism for the accuracy and the links between participants’ responses and actual behaviors or practices (Porter,
2011; LaNasa et al., 2009). There are certain circumstances that facilitate validity and reliability of a self-report survey: a) survey respondents have the knowledge to provide the information asked; b) the questions in the survey do not include potentially ambiguous terms or phrases; c) survey respondents believe the questions in the survey deserve thoughtful as well as serious responses; d) the questions in the survey ask about recent activities or practices; and e) the survey does not include questions that potentially result in embarrassment, threat, or violation of the survey respondents’ privacy or lead them to provide socially desirable responses (Kuh, 2001a; Kuh & Hu, 2001). ISSLA was adapted from NSSE, which was purposefully developed to fulfill every single of these circumstances (Kuh, 2004).
In the pilot study of ISSLA, a range of psychometric analyses were conducted to test the validity and reliability of the Engagement Indicator scales used in the survey(USAID, 2014). The study utilized the analytical methods of exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and internal consistency reliability
103 analysis. EFA showed that the majority of items loaded as expected onto the Engagement
Indicators, except for the items within the Supportive Environment scale. The EFA loadings indicated that many questions in the Supportive Environment scale were somewhat correlated to the Effective Teaching Practices scale. Besides, some other items loaded onto more than one scale.
The results of CFA showed a reasonable model fit with most items correlating well with their Engagement Indicators. Most items correlated strongly with their
Engagement Indicator. The exception to this was two items in the Collaborative Learning scale that had fairly low correlations with the underlying latent factor – Asked another student to help you understand course material and Worked with other students on course projects or assignments.
The results from reliability tests in the pilot study of ISSLA indicated that levels of internal consistency of the survey were sufficient, from .70 to .82. However, there were two engagement indicators came with low scores of reliability, collaborative learning (.47) and learning style (.60).
Overall, based on the results in the pilot study, the ISSLA developers claimed that the survey was relatively valid and reliable (USAID, 2014). However, in the current study, the validity and reliability of ISSLA were reexamined due to the different characteristics of the study sample. While undergraduates at three distinct institutional types were involved in the pilot of ISSLA, the current study focuses on students at public universities. In addition, criticism of the accuracy and internal consistency of NSSE in the United States (e.g., Campbell & Cabrera, 2011; LaNasa et al., 2009; Porter, 2011;
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Webber et al., 2013), from which ISSLA was developed, poses the need to reexamine the
ISSLA’s validity and reliability in this study. CFA was used to investigate the stability of ten engagement indicators of ISSLA. Also, reliability tests were undertaken to generate the alpha values for each engagement indicator. By investigating the accuracy and internal consistency of ISSLA in this study, the results could confirm whether some problems found in the ISSLA pilot study were isolated for the pilot sample within multiple institutional types or also occur in the sample of this study coming from a single institutional type. If the same issues remained, the use and interpretation of findings from this study had to be undertaken with caution by readers (Kuncel et al., 2005; Pike, 1996).
Data Collection
The administration of ISSLA is a collaborative effort between ISSLA staff and
ISSLA participating institutions over a 12-month timeline (ISSLA, 2014). The participating institutions in 2017 ISSLA were public four-year institutions and the survey administration was carried out online. ISSLA registration opened for institutions in early
June and closed at the end of September a year before the survey administration. ISSLA administration for each participating institution opened in spring 2017. ISSLA participating institutions provided the center that organized ISSLA access to a population data file including all and senior students, From the received data of students, the center selected a random sample for participation. Then, the center sent out emails of survey invitations along with other paperwork to selected students in February or March in 2017.
Reminder emails were sent out to non-respondents. In April, the participating institutions sent their student population file by including more information such as grade, major, and 105 enrollment year to the center by uploading the file to a secured web portal that belonged to the center. The survey administration closed in June 2017. In the beginning fall, the center sent individual institution report binders and data files back to participating institutions.
This study sought to examine the impact of student engagement on grades with the focus on first- and fourth-year undergraduates in Indonesian higher education. The information on student class level was obtained from the survey question, “What is your class level?” In this study, only those identified as first-year and senior students were included in the analysis.
Study Variables
Independent Variables
Student Engagement. Student engagement is essentially comprised of two major elements including student involvement in various educationally purposeful activities and support from institutions dedicated to enhancing students’ development and success during their college years (Hu & Kuh, 2002; Kuh, 2009). This study focuses on ten engagement indicators in ISSLA administered in 2017: higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment. These ten indicators, like in NSSE, have been considered as proxy measures for effective educational practices that promote student engagement in college (Kuh, 2001b; McCormick et al., 2013). In examining the impact of engagement on students’ GPAs, this study focused on two 106 approaches. First, each score of the indicators was analyzed as an individual independent variable constituting the concept of engagement in the college setting. Second, the ten indicators were utilized as a single variable of engagement. With these two approaches, the study could obtain a rounded understanding of the effects of student engagement represented by the ten engagement indicators as both an individual and a whole on GPA.
The score of each engagement indicator is an aggregation of responses to several items. All items in the engagement indicators used four-point response scales: 1 (Never/
Very little), 2 (Sometimes/ Some), 3 (Often/ Quite a bit), and 4 (Very often/ Very much), except for quality of interactions that provided seven-point responses in which 1 and 7 indicating poor and excellent respectively. Students’ four- or seven-point responses on individual items contributing to engagement indicators were converted to a 60-point scale in the ISSLA dataset and these responses were then averaged (USAID, 2014). A score of
0 on an engagement indicator would indicate that a respondent chose the lowest response option for each item, and 60 would mean that a student chose the highest response to every item. Table 3.2 shows a summary of the items and available responses.
Table 3. 2 Items within Each Engagement Indicator Engagement Items Responses Indicators During the current school year, how much has your coursework Higher-Order emphasized the following: 1 = Very little Learning Applying facts, theories, or methods to 2 = Some practical problems or new situations 3 = Quite a bit Analyzing an idea, experience, or line of 4 = Very much reasoning in depth by examining its parts
Continued 107
Table 3.2 Continued
Engagement Items Responses Indicators Forming a new idea or understanding from various pieces of information
Evaluating a point of view, decision, or information source During the current school year, how often have you: Combined ideas from different courses when completing assignments Connected your learning to societal problems or issues Included diverse perspectives (political, Reflective & religious, racial/ethnic, gender, etc.) in 1 = Never Integrative course discussions or assignments 2 = Sometimes Learning Examined the strengths and weaknesses of 3 = Often your own views on a topic or issue 4 = Very often Tried to better understand someone else’s views by imagining how an issue looks from his or her perspective Learned something that changed the way you understand an issue or concept Connected ideas from your courses to your prior experiences and knowledge During the current school year, how often have you: Learning Identified key information from reading 1 = Never Strategies assignments 2 = Sometimes Reviewed your notes after class 3 = Often Summarized what you learned in class or 4 = Very often from course materials During the current school year, how often have you: Reached conclusions based on your own Quantitative analysis of numerical information (numbers, 1 = Never Reasoning graphs, statistics, etc.) 2 = Sometimes
Used numerical information to examine a 3 = Often real-world problem or issue (unemployment, 4 = Very often climate change, public health, etc.)
Continued
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Table 3.2 Continued
Engagement Items Responses Indicators Evaluated what others have concluded from
numerical information During the current school year, how often have you: Asked another student to help you 1 = Never understand course material 2 = Sometimes Collaborative Explained course material to one or more 3 = Often Learning students 4 = Very often Prepared for exams by discussing or working through course material with other students Worked with other students on course projects or assignments During the current school year, how often have you had discussions with people from the following groups: 1 = Never People from a race or ethnicity other than 2 = Sometimes Discussions with your own 3 = Often Diverse Others People from an economic background other 4 = Very often than your own People with religious beliefs other than your own People with political views other than your own During the current school year, how often have you: Talked about career plans with a faculty member 1 = Never Student-Faculty Worked with a faculty member on activities 2 = Sometimes Interaction other than coursework (committees, student 3 = Often groups, etc.) 4 = Very often Discussed course topics, ideas, or concepts with a faculty member outside of class Discussed your academic performance with a faculty member Effective During the current school year, to what Teaching extent have your instructors done the Practices following:
Continued 109
Table 3.2 Continued
Engagement Items Responses Indicators Clearly explained course goals and requirements 1 = Very little Taught course sessions in an organized way 2 = Some Used examples or illustrations to explain 3 = Quite a bit difficult points 4 = Very much Provided feedback on a draft or work in progress Provided prompt and detailed feedback on tests or completed assignments Indicate the quality of your interactions with 1= Poor the following people at your institution: 2 Students 3 Quality of Academic advisors 4 Interactions Faculty 5 Student services staff (career services, 6 student activities, housing, etc.) 7= Excellent Other administrative staff and offices (registrar, financial aid, etc.) How much does your institution emphasize the following: Providing support to help students succeed academically Using learning support services (tutoring services, writing centre, etc.) Encouraging contact among students from 1 = Very little different backgrounds (social, racial/ethnic, 2 = Some religious, etc.) 3 = Quite a bit Supportive Providing opportunities to be involved 4 = Very much Environment socially
Providing support for your overall well- being (recreation, health care, counseling, etc.) Helping you manage your non-academic responsibilities (work, family, etc.) Attending campus activities and events (performing arts, athletic events, etc.) Attending events that address important social, economic, or political issues
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Instead of using the original engagement scores ranging from 0-60, this study transformed the scores into three different levels of engagement, low, medium, and high.
Those scored 0 to 20 were assigned into the low group, 21 to 40 into the medium group, and above 40 into the high group. The study used the transformed engagement variables in the entire analyses to examine the association between engagement and GPA, the relationship of engagement and background characteristics, and the effects of engagement on GPA.
Student Background Characteristics. The empirical evidence shows that background characteristics impact students’ behavior of participating in learning activities during their college years (Astin, 1993, 1999; Chickering & Gamson, 1987;
Pascarella, 2006; Pascarella & Terenzini, 2005). Thus, it is critical to control for demographic characteristics to gain net effects of student engagement on college grades.
The current study included a number of student characteristics as covariants. The selection of these characteristics was undertaken based on relevant literature. The student background characteristics that this study included were academic level, gender, major, working, and first-generation.
Academic level. The variable stemmed from the question of academic level or class standing. Only first- and fourth-year undergraduates were included in the study.
Freshmen became the reference group of the variable.
Gender. This variable was derived from the question, “What is your sex?”
Available responses were male and female. Female was used as the reference group.
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Major. This variable was evaluated by the item of “major” in the survey. The responses to the item were generated from an academic record prepared by each participating institution. From these responses, about 150 different academic disciplines were yielded. For the current study, the academic disciplines were categorized into
STEM and non-STEM majors with students majoring in non-STEM programs as the reference group.
Working. For this variable, students were categorized as working and not- working based on their responses to the questions in the ISSLA survey, “Hours per week:
Working for pay on campus” and “Hours per week: Working for pay off-campus”. Those respondents reported working at least for an hour off or on-campus every week were considered working students. Not working students were used as the reference group.
First-Generation. The empirical evidence indicates parent educational attainment affect the degree of student involvement in educational activities (Pike & Kuh, 2005). In comparison with continuing-generation students whose parents are college graduates, their first-generation peers who have no parents with college degrees have tendencies to be less engaged (Conway et al., 2011; Pascarella & Terenzini, 2005) Hence, the study utilized the status of first-generation as a control variable along with other demographic variables. The variable was derived from the ISSLA survey question, “What is the highest level of education that your mother/ father attained?” Student respondents were considered first-generation, a first family member enrolled in college if neither of their parents holds a college degree. Continuing-generation students were used as the reference group.
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Dependent Variable
Academic performance. Grade point average (GPA) with a 4-point scale which was provided by each participant institution in the ISSLA survey into two groups of
GPA, less sufficient (3.24 or lower ) and sufficientm (3.25 or above). Indonesian undergraduates mostly desire to achive a GPA of 3.25 or higher to persist, graduate, and land a desired job (Agustiani et al., 2016; Siang & Santoso, 2016) .
Analytical Methods
The data of ISSLA were collected by the center at Gadjah Mada University and the dataset was received with the de-identified student and institutional information. IBM
SPSS Statistics 25 was utilized in the data analysis for each research question this study attempted to address. To ensure the accuracy and consistency of the final results from the current study, data screening was performed before the obtained data were analyzed. The entire cases in the dataset came with complete responses or values. Hence, all of 1,602 cases were included in the final analyses.
Preliminary Analysis
Validity. In testing the validity of engagement indicators in ISSLA, a confirmatory factor analysis (CFA) was performed by using LISREL version 8.8.
Because 47 engagement items in the survey were ordinal with anchors of 4 and 7, the study used PRELIS to estimate both the correct polyserial correlation matrix and the related asymptotic covariance matrix. Afterward, to estimate the confirmatory factor model, weighted least square solution (WLS) was undertaken. It was critical to underline that in the WLS, both the correlation and asymptotic covariance matrices were employed 113 in entire estimation procedures. The use of the WLS was grounded on the scale of measurement that underlies the items in ISSLA. According to Joreskog and Sorbom
(2006), this method yielded correct standard errors as well as x2 values as normality was not met due to handling ordinal variables like the items of engagement in ISSLA.
The study relied on four robust measures of fit to judge the CFA model yields.
These indices include: (a) the Root Mean Square Error of Approximation (RMSEA), (b) the Comparative Fit Index (CFI), (c) the Non-Normed Fit Index (NNFI), and (d) the standardized root mean square residual (SRMR) (Kline, 2011). Goodness-of-fit values the study utilized were based on the guidance as recommended by Byrne (2006). In terms of RMSEA, the values had to be less than .08. Also, 90% (CI90) confidence intervals were estimated to examine that RMSEA values did not exceed the cut off value of .10 indicating a poor model fit. The values of CFI and NNFI had to be .95 or higher to achieve an excellent fit. However, the values greater than .90 were considered reasonably appropriate. The values below .08 were indicative of a good fit for SRMR.
Reliability. To examine the reliability of the ten constructs of engagement in
ISSLA, a Cronbach’s alpha reliability test was undertaken. Reliability was critical since variables built upon summated scales become predictors in objective models like the ten indicators of engagement in ISSLA. Variables derived from the instrument were considered reliable if stable responses were elicited over multiple times of administration
(Cronbach & Shavelson, 2004). According to Kline (2011), Cronbach’s alpha coefficients of about .90, .80, .70, and .60 indicated that the reliability of variables was excellent, very good, acceptable, and questionable respectively. The reliability with the
114 coefficient of.50 or below was considered unacceptable. However, with a sufficiently large sample size, somewhat lower degrees of reliability could be tolerated.
Primary Analysis
Specific analytical methods to address each research question in the current study are presented below.
1. What is the relationship between student engagement and GPA among Indonesian
undergraduates?
The descriptive and chi-square test analyses were performed to determine whether student engagement represented by the ten engagement indicators (higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment) and
GPA were correlated. The use of a chi-square approach was preferred to address the first research question for its capability to look at the relationship between categorical variables like the transformed engagement (low, medium, and high) and GPA (less sufficient and sufficient) variables in this study. To examine correlation, the chi-square test primarily measures frequencies that fall into each combination of categories constituting the tested variables (Field, 2017). The alpha level for the analysis was set as
α=.05.
2. What is the relationship between student engagement and student background
characteristics (academic level, gender, major, working, and first-generation)
among Indonesian undergraduates?
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To address the second research question, the analysis focused on testing the relationship between the ten engagement indicators and selected demographic variables including academic level (first-year and senior), gender (male and female), major (STEM and non-STEM), working (working and not working), and first-generation (first- generation and continuing-generation). Like the first research question, because the engagement and demographic variables were categorical, a series of descriptive and chi- square test analyses were performed.
3. Does student engagement have an impact on GPA among Indonesian
undergraduates after controlling for their background characteristics?
This study performed logistic regression to calculate separate models for
Indonesian undergraduates of the general effects of participation in educational activities
(low, medium, and high engagement) in college on grades (less sufficient and sufficient
GPA). The first model focused on estimating the impact of a set of student background characteristics (academic level, gender, major, working, and first-generation) on the participating students’ GPA. This initial model functioned as a statistical control method to initiate the isolation of the differential effects from the subsequent model. In the second model, only engagement indicators with significant relationships to GPA yielded in the first research question were inserted. The second model was intended to examine the extent to which engagement affects students’ chances of reaching higher college grades after controlling for the student background components. Then, the simultaneous evaluation was performed to estimate the effects of individual demographic and
116 engagement variables in the final model on academic grades as the remaining variables were constant.
Limitations
Despite a set of promising findings, this study had five primary limitations. First, the convenience sampling used by the ISSLA center might have influenced the random sample in this study. Ideally, to obtain a random sample, the center was supposed to randomly select students in Indonesian colleges and universities nationwide. However, in the case of ISSLA participation, institutions self-selected to register and administer
ISLLA on their campuses. Thus, the center obtained a random sample from the student population data file that each ISSLA participating institution provided. Therefore, the generalizability of this study might be a concern.
Second, this study involved students enrolled at public universities only. In
Indonesian higher education, public universities are typically more selective than other institutional types and a considerably desired destination for many Indonesians to pursue a college degree. Commonly, public universities have a diverse student body, particularly in terms of ethnicity, region of origin, socio-economic status, and study program.
Moreover, the universities in this study are situated in Java and Sumatera that enjoy better stability, infrastructure, public service, economy, and education as compared to other regions in Indonesia. Due to institutional type and location particularities lying on the universities involved in the study, the study findings were seemingly irrelevant as applied to other tertiary education institutions that enroll less diverse students, have lower
117 selectivity, come with different institutional type, and operate in outside of Java and
Sumatera.
Third, the survey this study utilized, ISSLA, heavily relied upon self-report responses. According to Kuh (2003), evidence shows that students are accurate, credible reporters of their activities and their collegiate experiences. However, findings of this study need to be interpreted with caution since self-report measures have an inherent bias such as recall and social desirability.
Fourth, this study utilized the engagement indicators of higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment in ISSLA to estimate students’ involvement in various learning activities in college. Each indicator was composed of three to eight different items in ISSLA. Because this study relied upon the engagement indicators, researchers and readers may not be able to obtain in-depth information about students’ specific engagement. For instance, the learning strategies indicator consists of three questions on respondents’ frequencies of using the strategies of identifying key information, taking notes, and summarizing course materials in their learning. However, in this study, learning strategies were treated as a single scale. Thus, it was difficult to look at the impact of each item in learning strategies on GPA and identify what type of learning strategies that had the strongest effect
Fifth, the study used a secondary dataset of the 2017 ISSLA. A limitation of secondary data use in research was that data were not purposefully collected to address
118 the research questions of this study. Thus, a key challenge was to ensure that the data appropriately addressed the research questions (Johnston, 2017). Other disadvantages of secondary data analysis may include errors occurred in the original survey that are no longer visible (interviewing, coding, and data entry), the inability to study sub- populations or assess the impact of an intervention in a particular institution, suspicious data quality (survey design and testing); and inhibition of creativity (Johnston, 2017).
Summary
This study sought to further understand the impact of student engagement on college grades among college students in Indonesia. This chapter highlights the research questions, a data source, a research instrument, research participants, variables, data analysis methods, and limitations of the study. In addition, it describes the research design and procedures of this study. The data were collected by the ISSLA organizer center in Spring 2017 from four public universities in Indonesia. There were 1,602 student participants who were included in the analyses. To answer the assigned research questions, this study primarily used descriptive (percentage), chi-square test, and logistic regression analyses. Descriptive statistics were intended to identify the demographic information of the student participants and the frequency distribution of each combination between two different variables. Then, chi-square tests were undertaken to examine the correlation between engagement and GPA as well as between engagement and student background characteristics of academic level, gender, major, working, and first-generation. Logistics regression analysis was utilized to examine the extent to which student engagement affected students’ likelihood of achieving higher college grades after 119 controlling for background characteristics. The results of the data analysis were presented in Chapter Four and then discussed in Chapter Five.
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Chapter 4. Results
This chapter is comprised of two primary sections. The first section presents the results from the preliminary analysis with the focus on the validity and reliability of the engagement items in ISSLA. The next section begins with descriptive information of students’ GPA and demographic characteristics including academic level, gender, major, working, and first-generation status. The last part of the second section is organized in accordance with the order of the research questions in the current study. The results corresponding each research question are presented. A brief summary of the essential findings concludes this chapter and, in turn, generates the context for the final chapter, which covers the extended discussions, implementations, and conclusions.
Preliminary Analysis
To examine the construct validity of the ten-factor structure of 47 engagement items in ISSLA, CFA was undertaken. Based on the results of the pilot study of ISSLA
(USAID, 2014) that produced a set of engagement indicators, the model hypothesized ten different factors including higher-order learning, reflective and integrative learning, learning strategies, quantitative reasoning, collaborative learning, discussion with diverse others, student-faculty interaction, effective teaching practices, quality of interactions, and supportive environment.
The results of CFA in testing the ten-factor model indicated a tenable relative fit with the acceptable NNFI (.94) as well as CFI (.94) values. However, for the model’s absolute fit, while the RMSEA (.05, CI90 = .056, .059) was supportive, the SRMR (.25)
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was poor. A further examination of factor results yielded significant correlations among
the ten constructs representing engagement indicators in ISSLA (see Table 4.2). The
highest correlation was found between collaborative learning and reflective and
integrative learning (.85), while the lowest correlation, which was moderate-sized, was
between discussion with diverse others and quality of interactions (.38).
Table 4. 1 Structural Correlations among the Engagement Indicators Indicator 1 2 3 4 5 6 7 8 9 1. Collaborative Learning 2. Reflective and Integrative .85 Learning 3. Student-Faculty Interaction .75 .76 4. Higher-Order Learning .67 .70 .65 5. Effective Teaching Practices .69 .68 .70 .67 6. Quantitative Reasoning .73 .76 .64 .67 .64 7. Discussions with Diverse Others .58 .63 .59 .50 .57 .50 8. Learning Strategies .70 .76 .71 .66 .72 .69 .61 9. Quality of Interactions .53 .52 .70 .55 .61 .40 .38 .59 10. Supportive Environment .62 .60 .68 .68 .77 .55 .54 .62 .59
Table 4.3 shows the factor loadings, which are the standardized regression
weights, as well as variance explained by engagement items corresponding to each of
engagement indicator. Also, the table presents the extent to which each engagement
indicator is reliable. Overall, examined engagement items revealed tenable loadings
above the recommended cut-off of .50 (Kline, 2011) and appeared to explain substantial
variance in the model. Once treated as a scale, most engagement indicators reported
alpha reliabilities at or above the .70 recommended threshold.
The collaborative learning indicator had the four items with the loadings close to
.70 or higher. The item of CLproject, Worked with other students on course projects or
assignments, was found of having the lowest loading (.67), while the highest loading
122 belonged to the item of CLstudy, Prepared for exams by discussing or working through course material with other students (.99). Most of the variance for the items was adequately explained and the item that reported the highest unexplained variance (54%) was CLproject. As a scale, the reliability of the collaborative learning indicator was relatively acceptable with a Cronbach’s alpha of .70.
For the reflective and integrative learning indicator, the seven items reported excellent loadings ranging from .74 to .86. These higher loadings suggest substantial variance for the items was considerably explained. The range of unexplained variance was from 25% for the item of RIsocietal, Connected your learning to societal problems or issues, to 45%.for the item of RIdiverse, Included diverse perspectives in course discussions or assignments. This engagement indicator of reflective and integrative learning reported an excellent reliability (α = .81).
The entire items for the student-faculty interaction indicator had tenable loadings with the range of .84 to .94. Hence, none of items left substantial variance unexplained.
The highest variance explained by the item of SFperform, Discussed your academic performance with a faculty member, (89%) while the lowest was the item of
SFotherwork, Worked with a faculty member on activities other than coursework, (75%).
Cronbach’s alpha for student-faculty interaction was .79 and hence this indicator was highly reliable.
The four items comprising the higher-order learning indicator reported the loadings at least .89. The significant variance of the factor was captured by these items ranging from 78% for the item of HOanalyze, Coursework emphasized: ANALYZING an idea, experience, or line of reasoning in depth by examining its parts, to 86% for the item
123 of HOform, Coursework emphasized: FORMING a new idea or understanding from various pieces of information. A very good reliability was revealed for higher-order learning a Cronbach’s alpha of .85.
Of the effective teaching practices, two items had loadings around .80 and the remaining reported loadings of .91 or above. The item of ETdrafth, Instructors: Provided feedback on a draft or work in progress, came with the least unexplained variance (7%) and the item of ETgoals, Instructors: Clearly explained course goals and requirements, was found of having the largest unexplained variance (27%). As a scale, the reliability of this indicator was relatively high (α = .83).
Compared to other engagement indicators, the quantitative reasoning indicator became the only indicator consisting of the items with the loadings higher than .90. While the highest loading was the item of QRevaluate, Evaluated what others have concluded from numerical information, (.98), the lowest was the item of QRconclude, Reached conclusions based on your own analysis of numerical information, (.92). Unsurprisingly, most of the variance was well explained by the items ranging from 84% to 97%. The quantitative reasoning indicator reported a highly-sized reliability (α = .84).
The indicator of discussions with diverse others had four items with the loadings ranging from .67 to .93. Of the four items, the item of DDreligion, Had discussions with people with religious beliefs other than your own, was found as the only item with the unexplained variance higher (54%) than what it could explain. Meanwhile, the largest variance was explained by the item of DDeconomic, Had discussions with people from an economic background other than your own, (87%). The discussion with diverse others indicator reported a reasonably acceptable reliability with a Cronbach’s alpha of .76.
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For the learning strategies, the three items reported the loadings of .88 to .94. The item of LSreading, Identified key information from reading assignments, successfully explained the largest variance (89%). On the contrary, the least variance was explained by the item of LSsummary, Summarized what you learned in class or from course materials, (78%). The results of the reliability test revealed that the learning strategies indicator was the only indicator with the reliability score below .70, but still close (α =
.69).
The range of the loadings for the five items constituting the quality of interactions indicator was from .75 to .99. The item of QIfaculty, Quality of interactions with faculty, reported the least unexplained variance (2%), while the item of QIstudent, Quality of interactions with students, left higher variance unexplained (44%). The engagement indicator of quality of interactions’ Cronbach’s alpha was .85 indicating a very good reliability.
The supportive environment indicator consists of eight items. The loading for each item was .70 or higher. Two items were found of having the highest loading, the item of SEdiverse, Institutional emphasis: Encouraging contact among students from different backgrounds, (.94) and the item of SEevents, Institutional emphasis: Attending events that address important social, economic, or political issues, (.94). The item of
SEacademic, Institutional emphasis: Providing support to help students succeed academically, reported the lowest loading (.76). The entire items explained the variance higher than what they could not explain ranging from 58% to 89%. In comparison to other engagement indicators, the reliability score of supportive environment was the highest (α = .88).
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Table 4.2 The Items and Indicators of Engagement in ISSLA
Indicators Item M SD Loading Variance Reliability Explained Error CLaskhelp 3.25 .675 .83 .69 .31 Collaborative CLexplain 2.86 .708 .83 .69 .31 .70 Learning CLstudy 2.99 .757 .92 .85 .15 CLproject 3.07 .733 .68 .46 .54 RIintegrate 2.74 .721 .85 .72 .28 RIsocietal 2.86 .730 .86 .75 .25 Reflective and RIdiverse 2.56 .807 .74 .55 .45 Integrative RIownview 2.69 .724 .86 .74 .26 .81 Learning RIperspect 3.00 .665 .79 .63 .37 RInewview 2.92 .681 .83 .69 .31 RIconnect 3.11 .663 .84 .71 .29 SFcareer 1.81 .775 .87 .75 .25 Student- SFotherwork 2.11 .869 .84 .70 .30 Faculty .80 SFdiscuss 2.03 .790 .93 .86 .14 Interaction SFperform 2.08 .800 .94 .89 .11 HOapply 3.10 .794 .89 .80 .20 Higher-Order HOanalyze 3.14 .777 .89 .78 .22 .85 Learning HOevaluate 3.08 .769 .92 .85 .15 HOform 3.11 .795 .93 .86 .14 ETgoals 3.00 .702 .86 .73 .27 Effective ETorganize 3.07 .656 .87 .76 .24 Teaching ETexample 3.05 .700 .91 .82 .18 .83 Practices ETdraftfb 2.80 .752 .96 .93 .07 ETfeedback 2.68 .784 . 95 .91 .09 QRconclude 2.62 .802 .95 .91 .09 Quantitative QRproblem 2.42 .811 .96 .91 .09 .84 Reasoning QRevaluate 2.36 .820 .98 .97 .03 DDrace 2.72 .924 .82 .68 .32 Discussions DDeconomic 3.18 .763 .93 .87 .13 with Diverse .76 DDreligion 2.60 1.004 .67 .46 .54 Others DDpolitical 2.82 .892 .82 .68 .32 LSreading 2.79 .723 .94 .89 .11 Learning LSnotes 2.80 .762 .90 .81 .19 .69 Strategies LSsummary 2.79 .835 .88 .78 .22 QIstudent 6.31 1.004 .75 .56 .44 QIadvisor 5.42 1.525 .89 .80 .20 Quality of QIfaculty 5.44 1.317 .99 .98 .02 .85 Interactions QIstaff 4.79 1.634 .79 .63 .37 QIadmin 4.65 1.658 .81 .65 .35
Continued
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Table 4.2 Continued
Indicators Item M SD Loading Variance Reliability Explained Error SEacademic 3.23 .762 .76 .58 .42 SElearnsup 2.70 .920 .86 .74 .26 SEdiverse 2.98 .868 .94 .89 .11 Supportive SEsocial 3.10 .807 .92 .84 .16 .88 Environment SEwellness 2.51 .952 .91 .82 .18 SEnonacad 2.32 .912 .89 .79 .21 SEactivities 2.73 .869 .91 .82 .18 SEevents 2.67 .845 .94 .88 .12
Primary Analysis
Participants’ Grades and Demographic Characteristics
Table 4.3 outlines the profile of 1,602 participants in this study. They were students enrolled in bachelor’s degree programs at four public universities in Indonesia.
The table describes the participants based on selected demographic characteristics, academic level, gender, major, working, and first-generation. For academic level, senior or fourth-year students constituted more than half of the total study participants reported lower GPA than first-year students. Female achieved higher grades and their proportion was relatively larger than their male peers. When examining the participants by their majors, those majoring in STEM programs were greatly outnumbered by their counterparts in non-STEM programs. As it came to grades, STEM students had lower
GPA than others studying non-STEM subjects.
Regarding the employment status, more participants reported that they did not work in the last academic year while the rest were employed either on or off-campus. In comparison to not working students, their working peers slightly scored higher for GPA.
Based on the participants’ responses to their parents’ educational attainment, students
127 whose either or both of parents gained tertiary education were the majority over those who were a first member going to college in their families. The increasing presence of first-generation students, particularly, in Indonesian public institutions is a result of the intensifying scholarship programs and the single tuition fee policy implemented by the government benefitting students from low-income families (Logli, 2016; Zein, 2017).
Although first-generation has been associated with lower grades (Conway et al., 2011), in this study they reported better GPA than other students whose either parent graduated from college.
Table 4. 3 Participants’ GPA and Demographic Characteristics (N=1602) GPA Demographic Participants characteristics M (SD) SE Min Max Academic Level First-year 699 (43.6%) 3.41 (0.29) 0.011 1.35 3.91 Senior 903 (56.4%) 3.37 (0.30) 0.010 1.40 3.98 Gender Female 885 (55.2%) 3.45 (0.25) 0.008 1.35 3.98 Male 717 (44.8%) 3.31 (0.33) 0.012 1.37 3.95 Major STEM 493 (30.8%) 3.30 (0.33) 0.014 1.37 3.89 Non-STEM 1109 (69.2%) 3.43 (0.27) 0.008 1.35 3.98 Working Status Working 651 (40.6%) 3.39 (0.31) 0.012 1.40 3.94 Not working 951 (59.4%) 3.39 (0.29) 0.009 1.35 3.98 First-Generation Status First-generation 441 (27.5%) 3.43 (0.28) 0.013 1.35 3.95 Continuing-generation 1,161 (72.5%) 3.37 (0.30) 0.009 1.37 3.98
Research Question 1: Relationship Between Student Engagement and GPA
among Indonesian Undergraduates
The first research question of the current study was: What is the relationship between student engagement and GPA among Indonesian undergraduates? Hence, the
128 focus of the analysis is to examine to what extent students’ participation in educationally purposeful activities is associated with their college grades. To address the first research question, descriptive (percentage) and chi-square test analyses were performed.
The results of chi-square tests revealed significant correlation between students’
GPA and certain engagement forms including collaborative learning, X2 (2, N = 1602) =
27.83, p <.001, discussions with diverse others, X2 (2, N = 1602) = 8.44, p <.05, and learning strategies, X2 (2, N = 1602) = 20.82, p <.001. As can be seen in Table 4.4, the proportion of students gaining a sufficient GPA with high engagement (43.6%) in collaborative learning surpassed their counterparts in the other GPA group who had the same engagement level (35.5%). On the contrary, as it comes to discussions with diverse others, the percentage for students who achieved sufficient grades and were highly engaged (28.7%) was significantly lower than other students with a less sufficient GPA and high engagement (35.8%). For the learning strategies indicator, the lowest proportion of the sufficient GPA group was less engaged undergraduates (13.5%), while for the other grade group, students with high engagement were the minority (20.9%).
Table 4. 4
Student Engagement by GPA (%)
Engagement Indicator GPA Low Medium High Total Collaborative Learning Sufficient 2.7 53.8 43.6 100 Less Sufficient 8.4 56.1 35.5 100
Discussions with Sufficient 16.6 54.7 28.7 100 Diverse Others Less Sufficient 17.9 46.4 35.8 100
Learning Strategies Sufficient 13.5 62.5 24.0 100 Less Sufficient 23.5 55.6 20.9 100
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Research Question 2: Relationship Between Student Engagement and
Student Background Characteristics among Indonesian Undergraduates
The second research question of this study was: What is the relationship between student engagement and student background characteristics (academic level, gender, major, working, and first-generation) among Indonesian undergraduates? To answer this question, the analysis is focused on testing the relationship between the ten engagement indicators and certain demographic variables including academic level (first-year and senior), gender (male and female), major (STEM and non-STEM), working status
(working and not working), and first-generation status (first-generation and continuing- generation). As the first research question, a series of descriptive and chi-square test analyses were undertaken. The results from these analyses, also, would reveal the extent to which the proportion differences of engagement by distinct student groups differ.
Based on the chi-square tests, academic level was significantly associated with the student-faculty interaction, X2 (2, N = 1602) = 36.11, p <.001, quantitative reasoning, X2
(2, N = 1602) = 6.31, p <.05, and quality of interactions indicators, X2 (2, N = 1602) =
44.52, p <.001. As seen in Figure 4.1, significant proportion differences were present between seniors and freshmen who reported high involvement in the indicators correlated to academic level. Seniors had higher proportion than their first-year peers for the entire correlated indicators: quality of interactions, quantitative reasoning, and student-faculty interaction.
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Figure 4. 1 Students with high engagement at each academic level group
The results of chi-square tests on gender and engagement indicated that gender was significantly correlated with six engagement indicators. The indicators include higher-order learning, X2 (2, N = 1602) = 12.91, p <.01, effective teaching practices, X2
(2, N = 1602) = 9.83, p <.01, discussions with diverse others, X2 (2, N = 1602) = 14.94, p
<.01, learning strategies, X2 (2, N = 1602) = 16.53, p <.001, quality of interactions, X2 (2,
N = 1602) = 20.77, p <.001, and supportive environment, X2 (2, N = 1602) = 11.04, p
<.01. Figure 4.2 outlines the proportion of students who highly engaged in higher-order learning, effective teaching practices, learning strategies, discussions with diverse others, quality of interactions, and supportive environment for male and female students. As compared to male students, female undergraduates had significantly higher proportion for all of these engagment indicators, except for discussions with diverse other.
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13
2
Figure 4. 2 Students with high engagement at each gender group
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Based on the results of chi-squares tests on engagement and major, significant association was unveiled between major and four engagement indicators of ISSLA: student-faculty interaction, X2 (2, N = 1602) = 12.00, p <.01, quantitative reasoning, X2
(2, N = 1602) = 10.51, p <.01, discussions with diverse others, X2 (2, N = 1602) = 8.59, p
<.05, and quality of interactions, X2 (2, N = 1602) = 10.61, p <.01. Figure 4.3 shows that more students majoring in STEM programs reported high involvement in the entire associated engagement indicators than their peers in other majors.
Figure 4. 3 Students with high engagement at major group
In accordance with the results of chi-square tests performed on engagement and working status, whether students worked or not was significantly associated with reflective and integrative learning, X2 (2, N = 1602) = 19.02, p <.001, student-faculty interaction, X2 (2, N = 1602) = 46.90, p <.001, higher-order learning, X2 (2, N = 1602) =
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6.62, p <.05, quantitative reasoning, X2 (2, N = 1602) = 19.20, p <.001, and quality of interactions, X2 (2, N = 1602) = 11.61, p <.01. Figure 4.4 highlight the proportion differences for students reporting high engagement in quality of interactions, quantitative reasoning, higher-order learning, student-faculty interactions, and reflective and integrative learning between working and not working students. For all of these engagement indicators, students who had job while studying displayed higher proportion as compared to their not working peers.
Figure 4. 4 Students with high engagement at each working status group
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The results of chi-square tests revealed that first-generation status and four engagement indicators were significantly correlated. These indicators were collaborative learning, X2 (2, N = 1602) = 8.93, p <.05, reflective and integrative learning, X2 (2, N =
1602) = 6.36, p <.05, effective teaching practices, X2 (2, N = 1602) = 6.39, p <.05, and discussions with diverse others, X2 (2, N = 1602) = 10.44, p <.01. Figure 4.5 shows that among the engagement indicators that significantly related to first-generation, only in effective teaching practices, students whose neither parent went to college had higher proportion of those with high involvement.
Figure 4. 5 Students with high engagement at first- and continuing-generation groups
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Research Question 3: The Impact of Student Engagement on GPA Among
Indonesian Undergraduates After Controlling for Their Background
Characteristics
The last research question to be addressed by this study was: Does student engagement have an impact on GPA among Indonesian undergraduates after controlling for their background characteristics? Logistic regression analysis was utilized to examine the effect of student participation in various educational practices inside and outside of the classroom on chances of gaining a sufficient GPA beyond their background characteristics.
In examining the net impact of student engagement on GPA beyond student background characteristics, two models were estimated that regressed achieving a sufficient GPA on a set of demographic variables (academic level, gender, major, working, and first-generation) and selected engagement variables. Based on the results regarding the first research question of this study, GPA was significantly correlated with collaborative learning, learning strategies, and discussions with diverse others. Moreover, the results of multicollinearity diagnostic analysis indicated that these three engagement indicators by having VIF values around 1 were noticeably independent. Due to their significant relationship with college grades and distinctiveness, collaborative learning, learning strategies, and discussions with diverse others were inserted into the final model of the logistic regression analyzing the net impact of student engagement on GPA.
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Table 4. 5 Logistic Regression Results for GPA on Student Background Characteristics and Student Engagement Model 1 Model 2 Variable Β Sig. OR Β Sig. OR
Academic Level -0.361 ** 0.697 -0.337 * 0.714 (senior) Gender (male) -1.119 *** 0.327 -1.050 *** 0.350 Major (STEM) -0.805 *** 0.447 -0.875 *** 0.417 Working Status 0.087 0.080 (working) First-generation status 0.447 ** 1.564 0.427 ** 1.533 (first-generation)
Learning Strategies 0.415 * 1.514 (medium) Learning Strategies 0.387 (high) Collaborative Learning 0.924 ** 2.520 (medium) Collaborative Learning 1.301 *** 3.674 (high) Discussions with 0.133 Diverse Others (medium) Discussions with -0.250 Diverse Others (high)
Constant 2.185 0.823
-2 Log 1558.697 *** 1521.625 *** Likelihood Likelihood Ratio 180.551 *** Cox & Snell R2 0.086 0.107 Nagelkerke R2 0.131 0.163 Percent correct 78.2 79.1
* p<0.05, ** p <0.01, *** p <0.001
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As can be seen in Table 4.5, Model 1 that includes merely student background characteristics could correctly classify 78.2% of the undergraduates in the study. In Table
4.5, Model 2 indicates what happened as different forms of engagement were included in the model in predicting students’ gain of a sufficient GPA. The final model could correctly assign 79.1% of the entire students. The inclusion of the engagement indicators of learning strategies, collaborative learning, and discussions with diverse others to the final model yielded a 0.9% increase over the previous model. These results suggest that even after controlling for background characteristics, students’ participation in educationally purposeful activities positively impacted their GPAs.
The results of Model 2 revealed that while remaining variables were constant, two engagement indicators were found of significantly predicting GPA, learning strategies
(medium) and collaborative learning (high and medium). Students who were moderately engaged in educational activities with an emphasis on effective learning strategies were
1.51 times more likely to gain a sufficient GPA when contrasted to their peers with low engagement. In collaborative learning, compared to students who were least engaged, other students with medium and high engagement displayed higher likelihood to achieve
GPA of above 3.24, 2.52 and 3.67 respectively.
Summary
In the current study, over half of the students were first-year, female, and not working. Moreover, students majoring in STEM programs and having parents without college education became minority groups. Once it came to college grades, first-year,
138 female, non-STEM, not working, and first-generation students had higher GPAs as compared to their peers.
For the student participants coming from a single institutional type (public university), the survey of ISSLA, which was the main instrument in the study, was valid as well as reliable. Even, the survey’s validity and reliability in this study were much improved as compared to the same survey that was piloted to students at three different institutional types.
Among Indonesian graduates, student engagement and GPA were significantly correlated. Specifically, the significant proportion differences between students with sufficient and less sufficient GPAs were found in three engagement indicators including collaborative learning, discussions with diverse others, and learning strategies. Once the correlation between student engagement and background characteristics was tested, academic level, gender, major, working, and first-generation were significantly associated with certain types of engagement.
The academic level had a significant relationship with the student-faculty interaction, quantitative reasoning, and quality of interactions indicators. The engagement indicators that significantly correlated to gender were higher-order learning, effective teaching practices, discussions with diverse others, learning strategies, quality of interactions, and supportive environment. A significant association was revealed between major and student-faculty interaction, quantitative reasoning, discussions with diverse others, and quality of interactions. Whether students had a job or not was significantly associated with reflective and integrative learning, student-faculty interaction, higher-
139 order learning, quantitative reasoning, and quality of interactions. The status of first- generation and four engagement indicators including collaborative learning, reflective and integrative learning, effective teaching practices, and discussions with diverse others were significantly correlated.
The effect of student engagement represented by collaborative learning, learning strategies, and discussions with diverse others on GPA beyond background characteristics was significant. However, the effects of demographic variables remained as engagement variables were included. While other variables were constant, only learning strategies and collaborative learning were the indicators with a significant impact on GPA. Students with medium engagement in learning strategies had a higher likelihood to achieve a sufficient GPA than their peers who were least engaged. Also, students who were moderately and highly engaged in collaborative learning were more likely to have higher grades as compared to other students who reported low engagement.
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Chapter 5. Discussion and Implications
This chapter mainly consists of four sections. The chapter begins by discussing the findings of the current study. In the next section, the practical implications of the study are presented. Then, the recommendation for future research is highlighted. The last section presents the conclusion of this study.
Discussion
It is critical to gain a solid understanding of the factors that contribute to student success in Indonesian higher education. While college grade has been widely used to indicate to what extent college students academically succeed, research on educational activities in the college setting and their effects on students’ GPAs remains limited in
Indonesia (Hardini & Adriani, 2018; Siang & Santoso, 2016; Sulisworo & Suryani,
2014). Besides, college grades have been reported responsible for many withdrawals among Indonesian undergraduates (Agustiani et al., 2016; Imran et al., 2013). Therefore, this study is crucial to address the literature gap on student engagement and the problematic academic grades in the context of Indonesian higher education by generating empirical evidence of the impact of students’ participation in educationally purposeful activities both inside and outside of the classroom on their GPAs. The findings derived from this study provide evidence of the factors that either enhance or hinder students’ academic success. Also, the findings can be a guidance for, particularly, policymakers, 141 administrators, or other practitioners in higher education in planning, organizing, and implementing supportive policies and services at institutional or broader levels.
In accordance with the findings from the primary analysis in the current study, there are three major themes discussed in this section: 1) evident association between student engagement and background characteristics (academic level, gender, major, working, and first-generation; 2) the engagement indicators with insignificant correlations with GPA; 3) the effects of student engagement on chances of gaining a sufficient GPA after controlling for the selected demographic variables.
Evident Association Between Student Engagement and Background
Characteristics
The study suggests a significant association between student engagement and demographic characteristics. The academic level had a correlation with the engagement indicators of student-faculty interaction, quantitative reasoning, and quality of interactions. Gender was associated with higher-order learning, effective teaching practices, discussions with diverse others, learning strategies, quality of interactions, and supportive environment. The engagement indicators that were correlated to major were student-faculty interaction, quantitative reasoning, discussions with diverse others, and quality of interactions. The relationship was revealed between working and the indicators of reflective and integrative learning, student-faculty interaction, higher-order learning, quantitative reasoning, and quality of interactions. First-generation and collaborative learning, reflective and integrative learning, effective teaching practices, and discussions with diverse others were correlated.
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For the academic level, first-year and senior students had a significantly different proportion of varying degrees of engagement in three indicators, student-faculty interaction, quantitative reasoning, and quality of interactions. In comparison with first- year students, more seniors reported medium and high engagement but a few whose engagement was low in these three indicators. This finding was supported by other studies revealing that seniors were more likely to have higher engagement than their first- year peers (Carini et al., 2006; NSSE, 2019; Pike & Kuh, 2005; Webber et al., 2013).
With more years in college, senior students are more likely to have a wide range of experiences inside and outside of the classroom.
In Indonesia, writing theses and preparing graduation as well as professional career allow seniors to have more opportunities to interact with faculty more often than freshmen not only inside but also outside class (Moeliodihardjo, 2014). Also, as the year in college increases, seniors know more individuals on campus and have more tasks or activities that require interaction with others other than their peers and faculty, such as student service and other administrative staff (Zein, 2017). For instance, many scholarship or funding offers on campus are intended for undergraduates with one or more years of college and thus, the scholarship application process more favors seniors than their first-year peers to have more interaction with other individuals on campus. In terms of curriculum, while coursework for freshmen puts more emphasis on general education, seniors take classes with more focus on the major. Commonly subjects that seniors learn in their classes are more complexed and expose them to activities of
143 evaluating and criticizing ideas or arguments by using numerical and statistical information (Logli, 2016; Zein, 2017).
With respect to gender, the proportion for those who reported high engagement significantly differs in higher-order learning, effective teaching practices, discussions with diverse others, learning strategies, quality of interactions, and supportive environment. Female students with high engagement had a higher proportion as compared to male students for these indicators, except for discussions with diverse others. This finding is linear with a study by Kinzie and colleagues (2007) identifying the gender gap in student engagement. The researchers found that female students participated more often in course-related activities such as preparing class presentations, reading materials, writing assigned papers, and accomplishing tasks that required to integrate as well as synthesize ideas from what they have learned in classes and other various sources. Meanwhile, male students were more likely to be engaged in activities beyond academic matters, such as relaxing, co-curricular events, physical activities, and socializing.
According to the cultural dimension of masculinity and femininity by Hofstede
(2001), Indonesia falls into the category of a masculine culture where there is a clear cut of roles and social expectations for males and females (Mangundjaya, 2013). This such culture puts males at an advantage in public and professional spheres (Riantoputra &
Gatari, 2017). Hence, education has been widely perceived as means for females to attain equal opportunities and recognition relative to their male counterparts (Samarakoon &
Parinduri, 2015). Given the importance of education, once female students are enrolled in
144 college, they have tendencies to more actively participate in academic related activities to meet or even outperform courses’ standards.
The results of the study suggest that students studying in STEM majors were more likely to have significantly higher engagement than their non-STEM peers in quantitative reasoning, student-faculty interaction, discussions with diverse others, and quality of interactions. The culture of field and program selectivity may explain engagement differences between STEM and non-STEM students.
Distinct cultures of STEM and non-STEM fields may result in differences in students’ participation in coursework that emphasized on quantitative reasoning. Brint and colleagues (2008) stated that as non-STEM studies are more focused on interaction, participation, exploration of ideas, and openness, STEM studies more relied on quantitative skill enhancement, collaborative works, and rigidity. Thus, undergraduates studying in STEM programs receive more exposure to learning activities that include identification, evaluation, and use of numeral as well as statistical information. In addition, since STEM majors use collaborative work more often, their students have higher chances to interact with faculty, peers, and other individuals on campus. Other than the program's cultural aspect, the program selectivity might explain engagement differences between STEM and non-STEM students. In Indonesia, STEM majors, particularly in public universities, are more selective than other programs, such as social science and education majors. Hence, students in STEM majors merely accounted for one-third of the total undergraduates in Indonesia (Sitepu, 2013). Due to the program’s selectivity, more students enrolled in STEM are those who have higher academic
145 achievement at high school, have parents with college degrees, and come from higher- income families (Clark, 2014). The literature on student engagement suggests that students with those aforementioned characteristics have more likelihood to participate in educational activities during college years than other students (Conway et al., 2011; Hu
& Wolniak, 2013; Pascarella & Terenzini, 2005; Salamonson et al., 2009).
For the status of working, the significant engagement differences between working and not working students were present in reflective and integrative learning, student-faculty interaction, higher-order learning, quantitative reasoning, and quality of interactions. The study suggests that students who had jobs either on or off-campus were likely to more engage in those such activities. Surprisingly, this finding contrasts what previous research revealed that working students tended to have lower engagement as compared to their peers who did not work (Astin, 1993; Kuh, Kinzie, Cruce, et al., 2007;
Salamonson et al., 2009). Since working students had to put their time and effort into their jobs, they were less likely to have sufficient chances to participate in academic related activities such as class preparation, studying, class attendance, and homework accomplishment.
Based on the results of this study, spending time on working was less likely to decrease Indonesian undergraduates’ participation in educationally purposed activities.
The job place and requirement might explain the unprecedented pattern of engagement between working and not working college students in Indonesia. Working on campus increases opportunities for students to interact with other individuals on campus more often (Imran et al., 2013; Zein, 2017). Hence, their engagement for student-faculty
146 interaction and quality of interactions may be higher than their peers who do not work while studying. Other than the place of working, the job requirement may also explain the finding regarding engagement degrees by working. In Indonesia, many vacancies for which ongoing college students are eligible put GPA as an important consideration
(Agustiani et al., 2016). Commonly, employers set up a minimum GPA of 3.00 to 3.25 as a job requirement for student applicants and thus, they are more likely to hire students with a higher GPA. Since engagement is positively associated with college grades (Carini et al., 2006; Fuller et al., 2011; Gordon et al., 2008; Melius, 2011), it is not surprising that the working students in the study had higher engagement than other students who did not spend their time to work.
Engagement differences between first-generation and continuing-generation students in the study were significant in collaborative learning, reflective and integrative learning, effective teaching practices, and discussions with diverse others. In comparison with students who had parents with a college degree, their first-generation peers were less likely to be engaged in these indicators, except for effective teaching practices.
Tendencies of first-generation students to less engage in educational studies than continuing-generation students are in line with an existing body of research indicating the engagement gap between students with parents who graduated and did not form college
(Conway et al., 2011, Kuh, Kinzie, Buckley, et al., 2007; Pascarella & Terenzini, 2005;
Richards, Elgazarri, Sugg, & Fowler, 2017; Soria & Stebleton, 2012).
First-generation students are more likely to come from low-income families (Bui,
2002). It is also the case for first-generation undergraduates in Indonesia. Due to a lack of
147 parental and financial supports, they tend to have limited information about college such as courses, admission, academic life, and available scholarship or financial assistance
(Zein, 2017). Thus, mostly first-generation students’ college preparation is inadequate.
Their lack of college preparation might result in their lower likelihood to be connected to the institution they attend and be engaged in educational activities that campus offers as their peers with parents graduating from college commonly do not experience those such hurdles (Richards et al., 2017; Soria & Stebleton, 2012). In Indonesia, although first- generation students receive financial support from the government or other sources, they may still struggle to fit in college and focus on their academic works. It is because they often experience delay in scholarship payment, while it is the only source they rely on to fund their study (Zein, 2017).
Although in the rest correlated engagement indicators, first-generation students had lower engagement than their continuing-generation counterparts, they more often devoted their time to effective teaching practices. It means they highly engage in courses where faculty provided clear explanation of goals and tasks, organized teaching activities, and detailed feedback for ongoing as well as completed works (USAID, 2014). Since first-generation students’ parents do not earn a high income and have any college experiences, class and instructor become the primary source for this group of students to academically succeed in college (Logli, 2016; Wicaksono & Friawan, 2011). First- generation students’ high reliance on faculty and course they attend may explain their high engagement in effective teaching practices.
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Less Relevant Engagement Indicators for GPA
The study revealed that without controls, GPA was significantly associated with certain engagement indicators, learning strategies, collaborative learning, and discussions with diverse others. The proportion distribution of different degrees of engagement at each GPA group suggests that higher participation in collaborative learning and learning strategies favor higher college grades. On the contrary, exposure to diversity less facilitates a sufficient GPA for Indonesian undergraduates.
Unlike learning strategies, collaborative learning, and discussions with others, the remaining engagement indicators seem unrelated to college grades for Indonesian undergraduates. Higher-order learning and reflective and integrative learning were some indicators that did not show any correlation with GPA. These two indicators were constituted of educational activities that represented deep approaches to learning (Laird,
Shoup, Kuh, & Schwarz, 2008).
Deep approaches result in deep learning in which students are intrinsically motivated to gain an understanding of the new phenomenon by challenging one’s own existing knowledge and engaging multiple perspectives. Students’ personal commitment to deep learning manifests in their ways of learning such as combining information from a variety of resources and contexts, reading widely, involving others in discussing and refining ideas, evaluating the connection between each piece of collected information and larger constructs, and subsequently applying knowledge generated from learning in more concrete situations (Biggs, 2003). Laird and colleagues (2008) found that college students who reported higher engagement in deep approaches achieved higher grades.
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With the current curriculum, faculty in Indonesian tertiary education institutions are encouraged to utilize pedagogical approaches with an emphasis on deep learning
(Mukimin et al., 2019). However, the use of deep approaches to learning remains less likely to happen in practices. Surface approaches still dominate instructional activities managed by faculty in Indonesia (Liem, 2016). In contrast to deep approaches, surface approaches notably emphasize the content of information and learning by ‘rote’ and memorization techniques (Tagg, 2003). In Indonesia, like in other Asian countries, surface learning might be due to students’ view of the teacher and assigned text as the definitive source of information, which, in turn, considerably determines their academic gains in class (Liem, 2016). The use of surface approaches leads students to see studying merely aiming at avoiding failure in a test or exam rather than grasping essential concepts and reflecting on how they relate to other information and apply to different contexts
(Laird et al., 2008). The minimal emphasis on deep learning in instructional activities may be a reason for insignificant association between high-order learning as well as reflective and integrative learning and college grades.
Another engagement indicator that did not significantly correlate with grades is quantitative reasoning. Different major cultures between STEM and non-STEM lead to different degrees of exposure students receive toward quantitative reasoning. While
STEM majors more focus on the enhancement of quantitative skills and use of collaborative study, non-STEM majors including the humanities, arts, and social sciences put emphasis on participation, interaction, interest, and exploration of ideas (Brint et al.,
2008). A study by Veenstra and colleagues (2008) confirmed this claim of by revealing
150 that students majoring in STEM received higher exposure to learning emphasizing quantitative skills and, in turn, they perceived this type of skill was significant, while social engagement was crucial for their peers in other majors. Since students have tendencies to participate in major-related activities and their participation in these activities positively impacts their academic gains (Hurtado et al., 2010), the magnitude of association between quantitative reasoning and grades may vary by major. As the significant correlation of quantitative reasoning and GPA may occur among STEM students, it is apparently untrue for their peers studying in other majors.
Student-faculty interaction was, also, found not having a significant correlation with grades. Among the ten indicators to measure student engagement in ISSLA, the study participants were less likely to engage in student-faculty interactions. Over half of them reported low involvement in this form of engagement. Other non-academic responsibilities and more teaching hours among faculty in Indonesia have barred them from routinely interacting with students outside class (Rakhmani & Siregar, 2016).
Besides, the notable power gap that happens between students and faculty discourages students to willingly talk to or work with faculty (Naibaho & Adi, 2012). Even, if students and faculty interact, typically their interactions take place to address administration or grade-related matters (Saputra & Yuniawan, 2012). The lack of supportive and substantive interactions between students and faculty might cause this form of engagement not to relate to students’ college grades.
The study found that quality of interactions did not associate with GPA. This indicator focused on students’ interactions with peers, academic advisors, faculty, student
151 service staff, and other administrative staff and offices. For Indonesian undergraduates, among the individuals on campus, peers become the ones they more frequently consult in addressing their academic challenges or course-related difficulties (Hartono, 2012).
Learning in a group is appealing to students since they find it more effective than working individually for working out an assignment and preparing a test or exam, which are the essential components constituting final grades (Wijayanti, 2012). Individuals other than peers become important to students as it comes to not course-related matters such as administration and finance (Rakhmani & Siregar, 2016). That student interacts with the majority of individuals mentioned in quality of interactions for something else than courses might explain this engagement indicators’ trivial relation to college grades.
Effective teaching practices were one of the engagement indicators with insignificant correlations with GPA. This finding contrasts with previous research suggesting that effective teaching that faculty manage in class significantly associates with higher college grades (Carini et al., 2006; Kuh et al., 2008). In ISSLA, effective teaching practices manifest in such instructional practices as providing clear explanation of course goals and requirements, teaching course materials in an organized way, utilizing examples in describing a complex concept, providing feedback on a draft or on- going as well as completed work.
Based on the results of the validity analysis on the items constituting the engagement indicators, the questions on provided feedback on a draft or work in progress and provided prompt and detailed feedback on tests or completed assignments were found of having the highest explained variance of the indicator of effective teaching
152 practices, 93% or 91% respectively. These findings indicated that these two instructional practices were central in effective teaching performed by faculty. Many students in
Indonesia view feedback from faculty on their assignment or exam as a taboo subject to be known by others (Syahreni & Waluyanti, 2007). To them, feedback may represent their weaknesses or inability to accomplish the assignment or test well. By having this view within the culture of protecting ‘face’, students feel uncomfortable to reveal or discuss the feedback with faculty or even their friends. Moreover, feedback with negative tone that faculty provide discourages students to follow up and use it in improving their work (Lin & Huang, 2012). Therefore, the view of students putting feedback as a weakness and the presence of less constructive feedback might minimize the association of effective teaching practices and grades.
This study spotted an insignificant relationship between supportive environment and grades. This finding is linear with a study by Fuller and colleagues (2011) that revealed that supportive environment was loosely correlated with cumulative GPA for both first-year and senior undergraduates. Among a variety of types of institutional support in supportive environment, there are only two that somewhat directly relate to academic matters, providing support to help students gain academic success and encouraging students to use available learning support services (Laird et al., 2008).
Meanwhile, other kinds of support are more focused on exposure to diversity, overall well-being, social involvement, non-academic responsibilities, and engagement in campus events for either entertainment or solution search for various issues purposes.
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Hence, supportive environment with minimal emphasis on study-related activities might result in its loose correlation with GPA as shown in this study.
The Net Effects of Student Engagement on GPA
The last primary finding of this study is the significant impact of student engagement on GPA while background characteristics were constant. Moreover, in comparison with students’ background characteristics, the degree of their engagement’s effects on GPA was nearly double. This confirms other studies on student engagement in more developed higher education systems suggesting that students’ participation in educational activities inside and outside class significantly affected their college grades and became more essential over their individual attributes (Carini et al., 2006; Fuller et al., 2011; Gordon et al., 2008; Hugh & Pace, 2003; Kuh, 2003; Kuh et al., 2008; Meliuns,
2011; Neves, 2018; Pascarella et al., 2004; Sweeney, 2016; Neves, 2018). Even,
McCormick and colleagues (2013) asserted that student engagement in educational activities in tertiary education has been found as a critical determinant of the college impact on students’ achievement, particularly cognitive development and knowledge requisition.
As the remaining variables were constant, among the three variables correlated to
GPA, only learning strategies and collaborative learning displayed significant effects on college grades. In regard to learning strategies, the study suggests that students’ participation in course-related activities including reading, essay, reflection, and material summary assignments, increases their likelihood to achieve a higher GPA. This finding confirmed other existing studies suggesting that engagement in educational activities
154 where students were exposed to effective learning strategies was associated with and could enhance their academic achievement (Bai & Pan, 2009; Kuh et al., 2008;
Yashuang, 2013).
The learning strategies indicator in ISSLA is composed of the questions on course-related tasks, including identifying main ideas and information from the assigned reading, reviewing notes regularly, and summarizing what has been discussed and learned either in the classroom or from course materials. These course-related tasks are commonly undertaken by instructors in Indonesian tertiary education as a follow-up of what students learn in the class and a means to ensure they can meet the assigned course objectives. Thus, time and effort spending on course-related tasks puts undergraduates at an advantage for their grades (Naibaho & Adi, 2012).
Although learning strategies had a stronger impact on students’ GPA, it was merely significant for those who reported medium engagement while other variables were held. Students with moderate engagement were about 1.5 times more likely to gain a GPA of 3.25 or above as compared to their peers whose engagement was low. The insignificant effects of high engagement in learning strategies might be explained by the correlation of academic workload and burnout. According to Maslach, Schaufeli, and
Leiter (2001), burnout is defined as a negative emotional state composed of a set of feelings of tiredness, cynicism, and inefficacy. Academic burnout substantially affects student success in a negative way (Stoliker & Lafreniere, 2015). Lin and Huang (2012) revealed that college students could experience academic burnout in their learning process due to assignment overload as well as academic pressure. Thus, stress and
155 tiredness the student participants felt due to their high participation in course-related assignments might be responsible for the insignificant effects of high engagement in the learning strategies indicator on GPA.
Collaborative learning was another engagement indicator that had significant effects on GPA. It is true for students who reported moderate and high engagement. After controlling for the remaining variables, students who were moderately engaged in collaborative learning were about 2.52 times more likely to achieve a higher GPA as compared to their peers with low engagement. Moreover, those whose engagement was high in the engagement indicator had over 3.67 times likelihood to gain a GPA of 3.25 or above. These results suggest that higher participation in collaborative learning favors chance increases in achieving higher college grades.
The significant effects of collaborative learning on GPA highlights the significance of more time and effort to be devoted to educational activities with an emphasis on collaboration among Indonesian undergraduates. These findings can be explained from two perspectives, culture and context. The cultural perspective for collaborative learning has been based on collectivism and power distance embedded within the society. Mangundjaya (2013) asserted that Indonesians had a strong tradition of communalism that encompassed a collectivistic orientation putting more emphasis on collective work and efforts over working individually toward accomplishing tasks. The collectivist culture encourages college students in Indonesia to perform collaboration with their peers more often in the learning process and to believe that collaborative
156 learning is a crucial determinant of their academic success in college (Kusumawati,
2013).
Power distance is another cultural aspect that may situate collaboration as an essential learning practice among Indonesian undergraduates. In Indonesia, high-power distance is apparent that puts individuals with more or lesser authority as not equals in their interactions (Koentjaraningrat, 1999). Naibaho and Adi (2012) suggested that this culture was manifested in educational practices in college where students tended to perceive faculty as the source of knowledge and believed that with their authority, faculty could not be criticized. In this situation, students become very hesitant to express their opinions and criticisms toward what the teacher instructs or discusses. Moreover, the unequal power prevents students from seeking assistance from faculty members and peers turn out to be a critical source of support to remedy their academic difficulties as well as challenges throughout years of college (Hartono, 2012). Thus, working together with peers becomes substantially important for Indonesian undergraduates.
In terms of contextual perspective, the increasingly global awareness of the importance of collaboration at work has inspired the government to put collaborative learning as an essential element in the most recent curriculum reform across the levels of education in Indonesia (Mukimin et al., 2019). In tertiary education, collaborative learning is appealing to both faculty and students due to its benefit of facilitating learning while such constrains as faculty’s availability and class size still occur (Arai &
Handayani, 2012; Faisal, Rahmat, Sitti, & Adnan, 2013; Wijayanti, 2012).
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In the Indonesian college setting, collaborative learning can be either instructor- or student-initiated. For collaborative learning that is initiated and structured by the faculty, students must follow the instruction from the faculty on how to interact and cooperate with one another (Wijayanti, 2012). Within this situation, interactions among students and between students and the faculty take place. Johnson, Johnson, and Smith
(2014) advocate that group reward based on the individual contribution of the group member is essential to the effectiveness of the faculty-initiated collaborative learning on student academic achievement by encouraging students to be more concerned with their peers’ learning improvement.
The initiative to carry out collaborative learning possibly comes from students
(Arai & Handayani, 2012). Thanh (2011) stated that this type of collaboration is spontaneous and student-centered in nature. The student-initiated collaborative learning is spontaneous with the absence of prior instruction or intervention from the teaching staff.
It is, also, more student-centered where students voluntarily form groups and they make a decision on what activities to be performed, what issues they will discuss, and where learning will take place. Typically, students perform their own collaborative learning outside the classroom. Different from collaborative learning instructed by the faculty with the emphasis on group reward, in the voluntary group learning, individual member’s commitment to learn and contribute to the group is an important factor determining the extent to which collaborative learning is impactful toward student academic gains
(Thanh, 2011).
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There is a wide range of activities that Indonesian undergraduates perform in their group learning. Faisal & colleagues (2013) suggest that based on what to achieve, the activities in collaborative learning can be classified into two different categories, avoiding and engaging activities. In the earlier category, group learning is primarily undertaken to reduce the amount of individual work of each student has to devote. The activities under this category include sharing materials, copying class notes, and working out assignments or examination questions. While engaging behavior is adopted, the collaboration aims to obtain a better understanding of the course material. Within this category, the activities are typically discussing the material, providing help to the group members in understanding difficult matters, and discussing how to effectively address assignments or questions.
In collaborative learning, regardless of the aforementioned initiators and aims, students have opportunities to discuss between equals and participate freely (Naibaho &
Adi, 2012; Wijayanti, 2012). The joint activities enable students to share and exchange ideas, think and explore through given problems, propose and assess potential solutions, and clarify and challenge thoughts. These processes make collaborative learning superior to individualistic or competitive learning in promoting student academic gains, particularly in acquiring cognitive skills, addressing problem-solving tasks, and understanding the content (Arai & Handayani, 2012; Faisal et al., 2013; Wijayanti, 2012).
Hence, students who display higher involvement in learning activities with peers have more likelihood to obtain higher college grades.
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Unlike learning strategies and collaborative learning, the net impact of discussion with diverse others on GPA was not significant. Engagement in interactions with other individuals from different backgrounds such as ethnicity, socio-economic status, religious belief, and political view did not seem to promote higher grades. Tension in conversation students have with others who are different from them may discourage them to benefit from exposure to diversity for learning gains (Fauria & Fuller, 2015). Moreover, Corwin and Cintrόn (2011) found that many undergraduates, especially freshmen, were more likely to spend more time in conversation with others who shared similarities and they reported higher academic achievement.
Students’ tendency to interact more with their peers who come from similar backgrounds can be explained by a culture in Indonesia where its people commonly have a high level of uncertainty avoidance (Mangundjaya, 2013). Indonesians are more likely to feel irritated or even threatened in situations they are not familiar with including interactions with others who are different from them. Also, in Indonesia with more emphasis on the culture of protecting ‘face’, people tend to avoid conflicts by minimizing to talk in person about controversial or sensitive issues and with someone else with different background characteristics or views (Kartika, 2008; Mangundjaya, 2013).
Although student engagement showed significant effects on gaining higher college grades, it did not completely diminish the impact of background characteristics.
Once demographic and engagement variables were put together to predict students’ chances of gaining a sufficient GPA, four of the background characteristics under study were found significant: academic level, gender, major, and first-generation. While other
160 variables were controlled for, first-year, female, non-STEM, and first-generation students had higher significant likelihood of achieving higher grades as compared to their counterparts.
The finding that the effects of background characteristics remained significant while engagement was present in the current study was linear to what Kuh and colleagues
(2008) found in their research on engagement of first-year students. The researchers revealed that a group of input variables of demographic characteristics and academic achievement as well as experiences at high school significantly explained higher variance of students’ grades than engagement variables. Also, although engagement variables were included, the significant effects of more demographic variables, some of which were gender, ethnicity, and socioeconomic persisted. The presence of peer-group effects implies that demographic attributes may mitigate the magnitude of engagement’s effects on grades (Fuller et al., 2011; Kuh et al., 2008). Moreover, Astin (1993, 1999) asserts that the significant peer-group effects towards learning outcomes while engagement exists indicates that college is less successful in eliminating or even reducing many stereotypical distinctions between different groups of students.
Implication for Practice
The current study contributes not only to the existing literature on student engagement, particularly in the context of struggling higher education system like
Indonesia, but also to educational practices in tertiary education such as instructional activities, student services. The following section presents several implications of this
161 study intended for individuals on campus including students, faculty, administrators, and staff for student affairs.
Introducing Student Engagement and Its Importance for College Grades.
The current study supports the idea that college grades among Indonesian undergraduates depend on their involvement in educational activities inside and outside class. This evidence is linear with the previous studies, which were mostly conducted outside
Indonesia. Hence, although the concept of student engagement was not originally developed within the context of Indonesia, it can be beneficial in increasing Indonesian students’ likelihood to obtain higher grades.
Due to the significance of student engagement to Indonesian undergraduates’
GPA, it is critical to introduce it to the community of tertiary education in Indonesia. The introduction of student engagement should reach faculty, administrators, and staff because they are the ones who work closely with students on campus. Online media, such as institutional websites and mobile applications, professional development, and regular meetings can serve as effective venues to introduce the concept of student engagement in college to administrative and academic personnel. Students, also, need to be informed about engagement and how they can benefit from it. Student engagement can be introduced to Indonesian undergraduates primarily through orientation, advising, and various kinds of media such as flyers, brochures, and posters distributed to campus facilities students frequently access such as a library, learning center, student affairs office, and administration office.
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Besides the institutions involved in this study, the introduction of student engagement might reach a wider audience within Indonesian higher education. Seminars, conferences, and workshops can be strategic sites to share the significance of students’ participation in educational activities on their academic gain. Introducing the idea of engagement and its effects on students’ grades will gradually lead to a collective understanding that can eventually encourage institutional leaders, staff, faculty, and students to spend their substantial effort fostering student engagement.
Assigning Proportional Work. Learning strategies was the engagement form that had stronger effects on GPA among Indonesian undergraduates. In the survey, learning strategies included pointing out key ideas from reading assignments, reviewing notes outside class, and making a summary of what was discussed and learned.
Performing these such practices significantly enhanced Indonesian undergraduates’ prospect of gaining a higher GPA regardless of their demographic characteristics.
However, the effects of learning strategies turned unimportant on grades for those with high participation in this engagement form. Students exceedingly devoting their time and effort to learning strategies reported the likelihood of obtaining higher grades that was insignificantly different from their peers with lower engagement in these activities.
The educational activities that the engagement indicator of learning strategies highlights are common practices Indonesian faculty use to follow up what has been discussed in class and make a part of assignments eventually constituting students’ final grades (Naibaho & Adi, 2012). Hence, spending effort on the activities in learning strategies benefits students for their GPAs. Nevertheless, once students receive too much
163 work, they might experience academic burnout that, in turn, causes the positive effects of assignment on grades to decrease or even turn opposite (Stoliker & Lafreniere, 2015).
This idea is in line with the results of this study showing an insignificant impact of learning strategies on GPA for Indonesian undergraduates.
Because excessive course-related assignment tends to be a stressor in Indonesian students’ lives and then might hinder their likelihood to achieve a higher GPA, students should be assigned to proportional work. The institutions or faculty have to limit the number of assignments or work for students. In designing and assigning work, faculty need to know and consider the workload students have in other classes. Faculty, also, should provide reasonable time for either online or in-person consultation as students have concerns or difficulties regarding assignments they are working. In addition, combining individual and group work can be undertaken to reduce potential academic burnout due to an abundance of the course-related tasks.
Fostering Collaborative Learning Inside and Outside of the Classroom.
While the remaining variables in the study were controlled for, collaborative learning came as the most influential engagement form in predicting Indonesian undergraduates’ grades. In ISSLA, collaborative learning focuses on joint learning activities aimed at helping to understand course material, preparing for exams or tests, and accomplishing course projects or assignments. Evidently, Indonesian students’ participation in these such activities increased their likelihood to achieve a higher GPA.
The literature suggests that collaborative learning promotes learning by creating opportunities for students to discuss and challenge ideas, analyze given problems, and
164 form and evaluate potential solutions (Wijayanti, 2012). Moreover, the presence of peer support is the distinctive feature with which collaborative learning more favors higher academic achievement among students than individualistic learning (Arai & Handayani,
2012; Faisal et al., 2013; Wijayanti, 2012). The positive effects of collaborative learning on students’ academic gain were reflected in the statistics yielded in this study.
The evidence from this study on the significance of collaborative learning to students’ college grades poses the need to expand this practice either inside or outside class in a more frequent and systematic manner. Faculty in Indonesian tertiary education should foster collaborative learning by rigidly making this practice one of the primary activities of their teaching. To enable students fully benefit from collaborative learning, faculty have to emphasize deep approaches to learning in instructional activities, assignments, projects, and exams, assess not only the group work but also individual contribution of the group member, and distribute higher achieving students evenly to each formed group of students.
Creating an inclusive campus environment. The group effects that this study found indicates the presence of stereotypes due to different background characteristics
(Astin, 1993). The stereotypes might heavily favor certain students over others. For instance, in this study with the context of Indonesian higher education, first-year, female, non-STEM, and first-generation students had more likelihood to achieve higher grades than their corresponding peers when other variables were held. The stereotypes that occur among different groups of students on campus might also mitigate their participation in
165 learning activities and the impact of their involvement on learning gains (Conway et al.,
2011; McCormick et al., 2013).
To ensure that students coming to campus with different background characteristics can have equal opportunities to involve in educationally purposeful activities and benefit from their engagement towards learning gains, it is critical to establish an inclusive campus environment. At least, there are three steps that institutions have to undertake in minimizing the stereotypes and establishing an inclusive campus environment that eventually can foster engagement of Indonesian undergraduates. First, institutions should know who their students are. Students come to campus with different background characteristics and previous experiences that possibly shape their behavior of engagement (Kuh, 2003; Quaye & Harper, 2014). Once institutions are adequately knowledgeable about their students, they can identify potential challenges that students may face during their college years and support needed to remedy these challenges.
Second, institutions have to be aware of the occurrence of stereotypes that potentially hinder certain students in their active participation in learning activities. With this awareness, institutions can treat students with greater caution and prioritize ones most affected by the stereotypes in their institutional efforts. After the thorough information of students has been collected and awareness of stereotypical differences for different students is established, the last step that institutions should do is providing the necessary support to create an inclusive campus environment. Policy, fund, facility, and student service are some forms of support that Indonesian tertiary institutions should
166 provide to allow all students, regardless of background characteristic and pre-college experience differences, to equally benefit from college.
Integrating student-faculty interaction into faculty promotion and reward.
Based on the results of this study, in comparison to other engagement forms, Indonesian undergraduates least engaged in interactions with faculty. Lower faculty wage and lack of institutional emphasis on student-faculty interaction may explain why students in
Indonesia are less likely to discuss the course materials, career plans, academic performance, and work with faculty on non-academic activities.
Rakhmani and Siregar (2016) stated that the majority of faculty in Indonesia monthly received about USD 300 or less from their service on campus. Consequently, working off-campus, for instance, as a consultant for the government as well as privates, is a choice for many faculty to seek additional income. Some faculty, also, work at more than one institution to make ends meet. When working off-campus or at multiple sites took up substantial time for faculty, it becomes difficult for faculty to serve and interact with their students after class adequately.
The lack of institutional emphasis on student-faculty interaction is another factor that might result in students’ low degree for this form of involvement. While research increasingly receives considerable attention from institutions, student-faculty interaction is not a widely discussed issue on campus (Altbach & Umakoshi, 2004; Logli, 2016). For instance, institutions commonly provide an incentive of USD 100 to 300 and USD 750 to
2,000 for one published paper in a nationally accredited and internationally recognized journal respectively. However, faculty receive only USD 30 to 100 for their service as a
167 thesis advisor. With the disproportioned appreciation for research publication and student-faculty interaction, faculty tend to focus their effort on research that favors them financially over interaction with students after class.
To foster student-faculty interaction among Indonesian undergraduates, institutions have to value faculty’s effort to engage and interact with students outside class. Providing rewards and valuing student-faculty interaction as a critical criterion in the promotion are suggested to encourage faculty to interact more often with students not only during but also after class. With the integration of student-faculty into faculty promotion and reward, faculty believe that their contribution to helping students is appreciated and become more motivated to provide students support regarding their academic and social life.
Implication for Research
Certain findings of this research warrant further research on engagement in
Indonesian higher education. First, in this study, the cultural aspect was potentially capable of explaining the findings that were different or contrast to the previous studies on engagement and its impact on college grades. For instance, by using the concept of culture dimensions suggested by Hofstede (2001), this study viewed that collectivism and power distance might explain that for Indonesian undergraduates, as compared to the remaining college indicators, collaborative learning exhibited a profound effect on GPA.
Meanwhile, the culture of protecting ‘face’ was seen useful in this study to elucidate why discussions with diverse others did not significantly improve Indonesian undergraduates’ chances of achieving a higher GPA after controlling for other variables. Due to the 168 significance of culture in explaining engagement in Indonesia, researchers are highly encouraged to consider and include culture in future studies to pursue a more comprehensive understanding of engagement and its effects on student learning in the context of Indonesian higher education.
Second, the study revealed that while engagement existed, the effects of certain background characteristics (academic level, gender, major, and first-generation) remained significant on GPA among Indonesian undergraduates. The presence of significant group effects on college grades needs an additional inquiry to examine the interactions between background characteristics and student engagement and the impact of these interactions on GPA. With the inclusion of interaction effects, whether the extent to which student engagement affects grades varies in magnitude by learners’ demographic characteristics in the Indonesian college setting can be addressed.
Third, in addition to the need for interaction effect examination, the significant effects of certain demographic characteristics that this study found indicate a need to study engagement within a group of students in Indonesian tertiary institutions. This such inquiry will result in a deeper understanding of engagement for different learner groups so that institutions can better provide the support that fits their varying needs. Previous research in a more developed higher education system on engagement of different student groups has led to various initiatives in many institutions to support diverse students and foster their participation in learning activities (Quaye & Harper, 2014).
Fourth, since this study only involved students at public universities in Java and
Sumatera islands, the findings cannot apply to other institutional types or other public
169 universities in other places. In the Indonesian context of higher education, different types of institutions have differences in terms of goals, student body, academic focus, and facility (Moeliodihardjo, 2014). Variations can, also, occur within a single institutional type due to location. Sumatera and Java islands where four public institutions involved in this study reside are a home to the majority of prominent universities and the desired destination for many Indonesian youths to pursue a college degree (Altbach & Umakoshi,
2004; Logli, 2016). Thus, public universities in these areas tend to have selectivity, faculty, and infrastructure above their counterparts in other islands. Due to differences in institutional type and location, it is necessary to undertake research on engagement in institutions other than a public university or other public universities in different areas.
Fifth, with the use of dataset from a single year in this study, to track the changes of each student’s engagement throughout the college years in Indonesia was not possible.
The participants in this study were senior and first-year students who were different individuals. Thus, what this study suggests based on the finding of higher engagement of seniors as compared to their freshman that the more years of student spend in college facilitates their engagement increase is loosened. The more precise information of engagement changes among Indonesian undergraduates over their study years can only be obtained from longitudinal research where the same individuals’ engagement at different times and its effects on their learning gains are examined (Astin, 1993). Fuller and colleagues’ (2011) study is an example of how a longitudinal study on engagement in college is undertaken. The researchers identified engagement changes among undergraduates in the United States in a more precise way by comparing their responses
170 to NSSE when they were a freshman and senior. Students’ participants in educational activities were more likely to increase as college years pass by. Interestingly, while first- year engagement had significant effects on students’ college grades, the effects became less significant when students turned senior. A study by Fuller and colleagues (2011) or other similar studies can serve as a reference to conduct another longitudinal study on engagement in the Indonesian context. Moreover, the results of the previous longitudinal research, which was mostly conducted in an established higher education system, yielded are worth examination whether these persist in Indonesian higher education or not.
Last, the instrument this utilized merely focuses on how often undergraduates participate in educational activities that are associated with learning outcomes (USAID,
2014). How students are engaged inside and outside of the classroom is less explored by the instrument. The lack of emphasis on quality of engagement in the instrument left certain findings, such as the modest impact of student engagement on GPA and negative effects that the engagement indicators of reflective and integrative learning, discussions with diverse others, effective teaching practices, and supportive environment, in this study minimally explained. To address this issue in future research, it is necessary to utilize a qualitative approach to understanding engagement among Indonesian undergraduates. Moreover, qualitative research on engagement can offer an in-depth understanding of student involvement and the way it impacts learning gains.
Conclusion
In Indonesia, an issue of undergraduates’ problematic grades is heavily associated with what occurs inside the classroom. Lack of effort made by students and ineffective 171 instructions are some concerns that are frequently perceived responsible for students’ lower GPA. An intensive body of literature on engagement suggests that the whole environment in college including inside and outside class is critical to students’ development over their years of study. Thus, this study was aimed to examine the effects of students’ participation in educationally relevant activities within and beyond classes on their college grades.
Based on the results of this study, the significant effects of student involvement in various educational activities inside and outside of the classroom on GPA are evident. It is in line with what other engagement studies revealed. However, different from previous studies on engagement, many of which were conducted in countries with more established higher education, that identified modest to large impact of engagement on grades, the effects this study found were significant but minor. Once demographic and engagement variables were included, group effects seemed to remain in predicting students’ chances of achieving a higher GPA. The presence of peer-group effects implied that demographic attributes might mitigate the magnitude of engagement’s effects on grades.
Among the engagement indicators in this study, learning strategies and collaborative learning were found of having the strongest effects on GPA after controlling for background characteristics. The cultural and instructional aspects may explain why learning strategies and collaborative learning are so critical for Indonesian undergraduates. The collectivist and power distance culture that places collaboration over individual work and peers as a major source for assistance in college encourages students
172 to work together with their peers more often. Meanwhile, the fact that many tertiary education institutions in Indonesia put more emphasis on course-related activities to promote students’ academic achievement underlies the importance of learning strategies to Indonesian undergraduates.
The evidence of effects of engagement on GPA that this study obtained can inform institutions what support students need to foster their participation in learning activities inside and outside class and benefit from it to enhance their learning gains.
Increasing access for Indonesian youth to higher education is critical but engaging them once they are enrolled becomes more important for their success not only during but also after college. To foster engagement among Indonesian college students requires substantial effort and commitment alongside collaborative work among policymakers, institutional leaders, faculty, and staff.
173
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Appendix A. Chi-Square Tests Engagement and GPA
Table A. 1 Chi-Square Tests Collaborative Learning and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 27.835a 2 .000 Likelihood Ratio 24.224 2 .000 Linear-by-Linear Association 16.911 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 14.08.
Table A. 2 Chi-Square Tests Reflective and Integrative Learning and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.864a 2 .394 Likelihood Ratio 1.802 2 .406 Linear-by-Linear Association 1.426 1 .232 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 18.55.
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Table A. 3 Chi-Square Tests Student-Faculty Interaction and GPA Asymptotic Significance (2- Value df sided) Pearson Chi-Square .074a 2 .964 Likelihood Ratio .074 2 .963 Linear-by-Linear Association .059 1 .808 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 19.22.
Table A. 4 Chi-Square Tests Higher-Order Learning and GPA Asymptotic Significance (2- Value df sided) Pearson Chi-Square 5.646a 2 .059 Likelihood Ratio 5.438 2 .066 Linear-by-Linear Association 5.131 1 .023 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 28.83.
Table A. 5 Chi-Square Tests Effective Teaching Practices and GPA Asymptotic Significance (2- Value df sided) Pearson Chi-Square .784a 2 .676 Likelihood Ratio .769 2 .681 Linear-by-Linear Association .020 1 .886 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 26.59.
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Table A. 6 Chi-Square Tests Quantitative Reasoning and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.712a 2 .258 Likelihood Ratio 2.690 2 .261 Linear-by-Linear Association 2.319 1 .128 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 47.82.
Table A. 7 Chi-Square Tests Discussion with Diverse Others and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 8.445a 2 .015 Likelihood Ratio 8.376 2 .015 Linear-by-Linear Association 2.072 1 .150 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 60.56.
Table A. 8 Chi-Square Tests Learning Strategies and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 20.817a 2 .000 Likelihood Ratio 19.288 2 .000 Linear-by-Linear Association 12.280 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 56.31. 208
Table A. 9 Chi-Square Tests Quality of Interactions and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .605a 2 .739 Likelihood Ratio .595 2 .743 Linear-by-Linear Association .556 1 .456 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 14.97.
Table A. 10 Chi-Square Tests Supportive Environment and GPA
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.234a 2 .540 Likelihood Ratio 1.229 2 .541 Linear-by-Linear Association .000 1 .988 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 52.52.
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Appendix B. Chi-Square Tests Engagement and Background Characteristics
Engagement and Academic Level
Table B. 1 Chi-Square Tests Collaborative Learning and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 4.319a 2 .115 Likelihood Ratio 4.432 2 .109 Linear-by-Linear Association .104 1 .747 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 27.49.
Table B. 2 Chi-Square Tests Reflective and Integrative Learning and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.020a 2 .364 Likelihood Ratio 2.007 2 .367 Linear-by-Linear Association 1.363 1 .243 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 36.22.
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Table B. 3 Chi-Square Tests Student-Faculty Interaction and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 36.111a 2 .000 Likelihood Ratio 37.241 2 .000 Linear-by-Linear Association 36.071 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 37.52.
Table B. 4 Chi-Square Tests Higher-Order Learning and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 4.693a 2 .096 Likelihood Ratio 4.691 2 .096 Linear-by-Linear Association 4.643 1 .031 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 56.29.
Table B. 5 Chi-Square Tests Effective Teaching Practices and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 3.872a 2 .144 Likelihood Ratio 3.860 2 .145 Linear-by-Linear Association .088 1 .767 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 51.92. 211
Table B. 6 Chi-Square Tests Quantitative Reasoning and Academic Level
Value df Asymptotic Significance (2-sided) Pearson Chi-Square 6.311a 2 .043 Likelihood Ratio 6.315 2 .043 Linear-by-Linear Association 6.160 1 .013 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 93.37.
Table B. 7 Chi-Square Tests Discussion with Diverse Others and Academic Level
Value df Asymptotic Significance (2-sided) Pearson Chi-Square .534a 2 .766 Likelihood Ratio .533 2 .766 Linear-by-Linear Association .073 1 .786 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 118.25.
Table B. 8 Chi-Square Tests Learning Strategies and Academic Level
Value df Asymptotic Significance (2-sided) Pearson Chi-Square .315a 2 .854 Likelihood Ratio .316 2 .854 Linear-by-Linear Association .069 1 .793 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 109.96.
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Table B. 9 Chi-Square Tests Quality of Interactions and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 44.524a 2 .000 Likelihood Ratio 44.492 2 .000 Linear-by-Linear Association 42.919 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 29.23.
Table B. 10 Chi-Square Tests Supportive Environment and Academic Level
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .830a 2 .660 Likelihood Ratio .831 2 .660 Linear-by-Linear Association .681 1 .409 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 102.54.
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Engagement and Gender
Table B. 11 Chi-Square Tests Collaborative Learning and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 5.251a 2 .072 Likelihood Ratio 5.219 2 .074 Linear-by-Linear Association 1.863 1 .172 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 28.20.
Table B. 12 Chi-Square Tests Reflective and Integrative Learning and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.821a 2 .244 Likelihood Ratio 2.816 2 .245 Linear-by-Linear Association .901 1 .343 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 37.15.
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Table B. 13 Chi-Square Tests Student-Faculty Interaction and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.539a 2 .281 Likelihood Ratio 2.534 2 .282 Linear-by-Linear Association 2.538 1 .111 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 38.49.
Table B. 14 Chi-Square Tests Higher-Order Learning and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 12.906a 2 .002 Likelihood Ratio 12.898 2 .002 Linear-by-Linear Association 12.879 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 57.74.
Table B. 15 Chi-Square Tests Effective Teaching Practices and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 9.826a 2 .007 Likelihood Ratio 9.852 2 .007 Linear-by-Linear Association 9.817 1 .002 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 53.26.
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Table B. 16 Chi-Square Tests Quantitative Reasoning and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.855a 2 .396 Likelihood Ratio 1.847 2 .397 Linear-by-Linear Association .997 1 .318 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 95.78.
Table B. 17 Chi-Square Tests Discussion with Diverse Others and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 14.938a 2 .001 Likelihood Ratio 14.898 2 .001 Linear-by-Linear Association 8.548 1 .003 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 121.29.
Table B. 18 Chi-Square Tests Learning Strategies and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 16.531a 2 .000 Likelihood Ratio 16.465 2 .000 Linear-by-Linear Association 13.630 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 112.79.
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Table B. 19 Chi-Square Tests Quality of Interactions and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 20.774a 2 .000 Likelihood Ratio 20.749 2 .000 Linear-by-Linear Association 20.601 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 29.99.
Table B. 20 Chi-Square Tests Supportive Environment and Gender
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 11.041a 2 .004 Likelihood Ratio 11.082 2 .004 Linear-by-Linear Association 10.604 1 .001 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 105.18.
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Engagement and Major
Table B. 21 Chi-Square Tests Collaborative Learning and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 4.756a 2 .093 Likelihood Ratio 4.761 2 .092 Linear-by-Linear Association 4.728 1 .030 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 19.39.
Table B. 22 Chi-Square Tests Reflective and Integrative Learning and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 5.387a 2 .068 Likelihood Ratio 5.116 2 .077 Linear-by-Linear Association 2.094 1 .148 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 25.54.
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Table B. 23 Chi-Square Tests Student-Faculty Interaction and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 12.000a 2 .002 Likelihood Ratio 11.735 2 .003 Linear-by-Linear Association 11.978 1 .001 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 26.47.
Table B. 24 Chi-Square Tests Higher-Order Learning and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 3.457a 2 .178 Likelihood Ratio 3.436 2 .179 Linear-by-Linear Association .049 1 .825 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 39.70.
Table B. 25 Chi-Square Tests Effective Teaching Practices and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .379a 2 .828 Likelihood Ratio .377 2 .828 Linear-by-Linear Association .352 1 .553 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 36.62.
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Table B. 26 Chi-Square Tests Quantitative Reasoning and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 10.511a 2 .005 Likelihood Ratio 10.653 2 .005 Linear-by-Linear Association 6.590 1 .010 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 65.86.
Table B. 27 Chi-Square Tests Discussion with Diverse Others and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 8.592a 2 .014 Likelihood Ratio 8.820 2 .012 Linear-by-Linear Association 7.965 1 .005 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 83.40.
Table B. 28 Chi-Square Tests Learning Strategies and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.988a 2 .370 Likelihood Ratio 1.966 2 .374 Linear-by-Linear Association 1.590 1 .207 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 77.55.
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Table B. 29 Chi-Square Tests Quality of Interactions and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 10.611a 2 .005 Likelihood Ratio 10.802 2 .005 Linear-by-Linear Association 10.446 1 .001 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 20.62.
Table B. 30 Chi-Square Tests Supportive Environment and Major
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .926a 2 .629 Likelihood Ratio .925 2 .630 Linear-by-Linear Association .012 1 .912 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 72.32.
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Engagement and Working
Table B. 31 Chi-Square Tests Collaborative Learning and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.433a 2 .296 Likelihood Ratio 2.416 2 .299 Linear-by-Linear Association .115 1 .734 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 25.60.
Table B. 32 Chi-Square Tests Reflective and Integrative Learning and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 19.024a 2 .000 Likelihood Ratio 18.851 2 .000 Linear-by-Linear Association 13.143 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 33.73.
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Table B. 33 Chi-Square Tests Student-Faculty Interaction and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 46.902a 2 .000 Likelihood Ratio 46.519 2 .000 Linear-by-Linear Association 46.585 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 34.95.
Table B. 34 Chi-Square Tests Higher-Order Learning and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 6.620a 2 .037 Likelihood Ratio 6.653 2 .036 Linear-by-Linear Association 6.597 1 .010 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 52.42.
Table B. 35 Chi-Square Tests Effective Teaching Practices and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .808a 2 .668 Likelihood Ratio .815 2 .665 Linear-by-Linear Association .075 1 .784 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 48.36.
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Table B. 36 Chi-Square Tests Quantitative Reasoning and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 19.197a 2 .000 Likelihood Ratio 19.371 2 .000 Linear-by-Linear Association 17.378 1 .000 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 86.96.
Table B. 37 Chi-Square Tests Discussion with Diverse Others and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.896a 2 .388 Likelihood Ratio 1.912 2 .384 Linear-by-Linear Association .968 1 .325 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 110.13.
Table B. 38 Chi-Square Tests Learning Strategies and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.312a 2 .519 Likelihood Ratio 1.317 2 .518 Linear-by-Linear Association 1.210 1 .271 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 102.40.
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Table B. 39 Chi-Square Tests Quality of Interactions and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 11.615a 2 .003 Likelihood Ratio 11.703 2 .003 Linear-by-Linear Association 11.116 1 .001 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 27.23.
Table B. 40 Chi-Square Tests Supportive Environment and Working
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.024a 2 .363 Likelihood Ratio 2.034 2 .362 Linear-by-Linear Association 1.942 1 .163 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 95.50.
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Engagement and First-Generation
Table B. 41 Chi-Square Tests Collaborative Learning and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 8.935a 2 .011 Likelihood Ratio 8.997 2 .011 Linear-by-Linear Association 5.653 1 .017 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 17.34.
Table B. 42 Chi-Square Tests Reflective and Integrative Learning and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 6.363a 2 .042 Likelihood Ratio 6.430 2 .040 Linear-by-Linear Association 6.352 1 .012 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 22.85.
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Table B. 43 Chi-Square Tests Student-Faculty Interaction and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .150a 2 .928 Likelihood Ratio .150 2 .928 Linear-by-Linear Association .027 1 .870 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 23.67.
Table B. 44 Chi-Square Tests Higher-Order Learning and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.262a 2 .532 Likelihood Ratio 1.260 2 .532 Linear-by-Linear Association .605 1 .437 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 35.51.
Table B. 45 Chi-Square Tests Effective Teaching Practices and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 6.390a 2 .041 Likelihood Ratio 6.938 2 .031 Linear-by-Linear Association 3.167 1 .075 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 32.76.
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Table B. 46 Chi-Square Tests Quantitative Reasoning and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 4.722a 2 .094 Likelihood Ratio 4.761 2 .093 Linear-by-Linear Association .002 1 .966 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 58.91.
Table B. 47 Chi-Square Tests Discussion with Diverse Others and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 10.444a 2 .005 Likelihood Ratio 10.729 2 .005 Linear-by-Linear Association 7.012 1 .008 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 74.60.
Table B. 48 Chi-Square Tests Learning Strategies and First- Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 2.810a 2 .245 Likelihood Ratio 2.878 2 .237 Linear-by-Linear Association .720 1 .396 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 69.37.
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Table B. 49 Chi-Square Tests Quality of Interactions and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square .026a 2 .987 Likelihood Ratio .025 2 .987 Linear-by-Linear Association .014 1 .905 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 18.44.
Table B. 50 Chi-Square Tests Supportive Environment and First-Generation
Asymptotic Significance (2- Value df sided) Pearson Chi-Square 1.828a 2 .401 Likelihood Ratio 1.855 2 .396 Linear-by-Linear Association 1.679 1 .195 N of Valid Cases 1602 a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 64.69.
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Appendix C. Logistic Regression
Block 1
Table C. 1 Block 1: Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 143.478 5 .000 Block 143.478 5 .000 Model 143.478 5 .000
Table C. 2 Block 1: Model Summary
Cox & Snell R Nagelkerke R Step -2 Log likelihood Square Square 1 1558.697a .086 .131 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.
Table C. 3 Block 1: Hosmer and Lemeshow Test
Step Chi-square df Sig. 1 8.851 8 .355
230
Table C. 4 Block 1: Classification Tablea
Predicted gpa_rec2 Percentage Observed 3.24 or below 3.25 or above Correct Step 1 gpa_rec2 3.24 or below 64 294 17.9 3.25 or above 55 1189 95.6 Overall Percentage 78.2 a. The cut value is .500
Table C. 5 Block 1: Variables in the Equation
B S.E. Wald df Sig. Exp(B) Step 1a Academic Level(1) -.361 .132 7.442 1 .006 .697 Gender(1) - .129 74.698 1 .000 .327 1.119 Major(1) -.805 .130 38.568 1 .000 .447 Working(1) .087 .130 .452 1 .502 1.091 First-Generation(1) .447 .154 8.455 1 .004 1.564 Constant 2.185 .148 219.031 1 .000 8.891 a. Variable(s) entered on step 1: Academic Level, Gender, Major, Working, First-Generation.
Table C. 6 Block 1: Correlation Matrix
Academic First- Constant Level(1) Gender(1) Major(1) Working(1) Generation(1) Step 1 Constant 1.000 -.523 -.537 -.342 -.213 -.251 Academic Level(1) -.523 1.000 .118 -.076 -.174 .041 Gender(1) -.537 .118 1.000 .007 -.085 -.065 Major(1) -.342 -.076 .007 1.000 -.021 .127 Working(1) -.213 -.174 -.085 -.021 1.000 -.034 First-Generation(1) -.251 .041 -.065 .127 -.034 1.000
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Block 2
Table C. 7 Block 2: Omnibus Tests of Model Coefficients
Chi-square df Sig. Step 1 Step 37.073 6 .000 Block 37.073 6 .000 Model 180.551 11 .000
Table C. 8 Block 2:Model Summary
Cox & Snell R Nagelkerke R Step -2 Log likelihood Square Square 1 1521.625 .107 .163 a. Estimation terminated at iteration number 5 because parameter estimates changed by less than .001.
Table C. 9 Block 2:Hosmer and Lemeshow Test
Step Chi-square df Sig. 1 7.503 8 .483
Table C. 10 Block 2:Classification Tablea
Predicted gpa_rec2 Percentage Observed 3.24 or below 3.25 or above Correct Step 1 gpa_rec2 3.24 or below 53 305 14.8 3.25 or above 30 1214 97.6 Overall Percentage 79.1 a. The cut value is .500
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Table C. 11 Block 2:Variables in the Equation
B S.E. Wald df Sig. Exp(B) Step 1a Academic Level(1) -.337 .134 6.337 1 .012 .714 Gender(1) -1.050 .132 63.388 1 .000 .350 Major(1) -.875 .133 43.474 1 .000 .417 Working(1) .080 .132 .363 1 .547 1.083 First-Generation(1) .427 .157 7.412 1 .006 1.533 LS_medium .415 .172 5.808 1 .016 1.514 LS_high .387 .214 3.274 1 .070 1.473 CL_medium .924 .288 10.307 1 .001 2.520 CL_high 1.301 .305 18.169 1 .000 3.674 DD_medium .133 .183 .528 1 .468 1.142 DD_high -.250 .200 1.561 1 .212 .779 Constant .823 .323 6.498 1 .011 2.277 a. Variable(s) entered on step 1: LS_medium, LS_high, CL_medium, CL_high, DD_medium, DD_high.
233