R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Explanatory Cross-Sectional Analysis on Achievement Emotions in Mathematics Using Achievement Goal and Kolb’s Learning Style Frameworks

AVELINO G. IGNACIO JR. State University City of , Bulacan, Email: [email protected]

Abstract This research work is an explanatory cross-sectional analysis on achievement emotions in mathematics. The main objectives of the study are as follows: (1) to test whether there is a significant interaction effect between the approach-avoidance dimension of achievement goals and Kolb’s learning styles on achievement emotions in mathematics, (2) to test whether there is a significant difference in the means of achievement emotions in mathematics when grouped according to approach-avoidance dimension of achievement goals, and (3) when grouped according to Kolb’s learning styles, and (4) to provide effect-size estimates for each significance test. Three instruments were utilized in the study, namely, Achievement Goal Questionnaire , Kolb’s Learning Style Inventory 3.1 , and Achievement Emotions Questionnaire for Mathematics . The study utilized cluster sampling , two-way

ANOVA interpreted through p-values (p), and partial eta-squared for effect-size estimates. Results show that there is a small interaction effect attributed to chance on all achievement emotions

which can only be seen through careful study. Similarly, there is an average

effect on enjoyment , and pride carried by achievement

goals, and an average effect on boredom , and anger carried by Kolb’s learning styles; which implies that both effects are somewhat visible in naked eye, and evidently, not attributed to chance. Consequently, mediation and moderation analysis linking achievement goals to positive achievement-related outcome emotions, and Kolb’s learning styles to negative achievement-related activity emotions, respectively, are recommended.

Keywords: Achievement Emotions, Achievement Goals, Learning Styles, Mathematics.

Introduction

Since mathematics education is characterized to be in its early years, and as well as an open concept that could go in various perspectives (Sriraman, 2016; Skovsmose, 2016, 2011), many mathematics educators and researchers have initiated to strengthen this developing research area from various contextual perspectives (D’ Ambrosio, 2016; Earnest, 2016a, 2016b, 2004; Zhao, 2016; Ignacio & Reyes, 2017; Ignacio, 2016; Ignacio & Policarpio, 2016). As a consequence, these provide fresh views on the theory and practice of mathematics teaching and learning (Earnest, 2004; Zhao, 2016; Le et al., 2006; Evans, 2001; Howson, Keitel, & Kilpatrick, 2008). Accordingly, how the education runs inside and outside classrooms must be, over and over again, reviewed and assessed by both educators and students (Ignacio & Santos, 2017).

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I In a previous study conducted by Ignacio (2016), more than half of the coded responses in a qualitative study involving engineering students were related to teaching styles, where most of the subthemes were rooted on the need to properly motivate students in mathematics subjects. This leads to the notion of diverse constructs linking to academic motivation inside mathematics classroom regardless of the program of study, whether it is a mathematically inclined course or not.

Ignacio and Policarpio (2016) presupposed a need to study appropriate motivation. And since individuals have a natural tendency to select certain goals, whether to approach or to avoid certain things, just to obtain favorable achievement situation, soon enough, individual will learn to value the consequences of certain outcomes. Thus, they examined the achievement goals of high school students. While the male respondents with highest mean mathematics performance in class generally hold performance-avoidance goals, female respondents with highest mean mathematics performance in class generally hold mastery-avoidance goals. In this case, those who hold the goal of avoiding unfavorable situations possessed the relative highest mean performance. And this relationship was statistically significant, meaning, it is not attributed to chance.

Ignacio and Reyes (2017) further explored this scenario through the inclusion of another construct known as Kolb’s (2005) learning styles. Learning style is the way in which individuals begin to concentrate, process, internalize, and retain new and difficult academic information, moreover, these are different approaches students use in perceiving and processing information (Kolb, 1984). Accordingly, Ignacio and Reyes (2017) found out that students with approach type of mathematics achievement goal mostly learn by reflecting on abstract concepts and putting the information in logical form. On the other hand, students with avoidance type of mathematics achievement goal mostly take concrete experiences mixed with active experimentation in a hands-on experience. Individuals rely more heavily on people for information than on their own technical analysis. But this structure was not statistically significant. In line with this, the present researcher was not yet satisfied with the earlier results.

In conjunction with the outcomes of previous researches, as a consequence, since academic settings, remarkably in mathematics education, abound with achievement emotions such as enjoyment of learning, pride, anger, anxiety, shame, hopelessness, or boredom; and that undeniably, these emotions are critically important for students’ motivation, learning, and performance (Pekrun, Goetz, Frenzel, Barchfield, & Perry, 2011; Zeidner, 2007); given that research on human emotions in education is in a state of fragmentation (Pekrun, 2006); this research endeavor will try to determine the main and interaction effects, along with the effect sizes, of achievement goals (Elliot & Murayama, 2008), and Kolb’s (1985) learning styles, on achievement emotions in mathematics (Pekrun et al., 2011).

Theoretical Framework

Control-Value Theory of Achievement Emotions

Most emotions pertaining to attending class, studying, and writing tests and exams are seen as achievement emotions (Sanchez-Rosas & Furlan 2017; Pekrun & Perry, 2014). Achievement emotions are defined as emotions tied directly to either achievement activities that pertain to ongoing achievement-related activities, or achievement outcomes that pertain to the outcomes of these activities (Pekrun et al., 2011; Pekrun, Elliot, & Maier, 2006) that can affect students’ learning and performance, through the mediating effects of motivation, strategy use, and regulation of learning (Pekrun, 2006).

And this is primarily grounded on control-value theory of achievement emotions which hypothesized that achievement emotions are produced when the individual feels in control of, or out of control of, activities and outcomes that are subjectively valuable, which implies that appraisals of control and value significantly triggers these emotions (Pekrun, Goetz, Titz, & Perry, 2002a, 2002b).

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Pertaining to achievement-related activities and outcomes, control appraisals relate to its perceived controllability, while value appraisals relate to its subjective importance (Pekrun, 2006). Furthermore, emotions influence students’ intrinsic motivation to learn, extrinsic motivation related to the attainment of positive outcomes or to the prevention of negative outcomes (Linnenbrink-Garcia & Pekrun, 2011), and promote different styles of regulation of learning (Pekrun, Elliot, & Maier, 2006; Pekrun et al., 2011). Moreover, there are already seven established achievement emotions in mathematics (Pekrun, Goetz, & Frenzel, 2005b), namely, enjoyment of learning, pride, anger, anxiety, shame, hopelessness, and boredom. Enjoyment and pride are considered positive outcome emotions; anger and boredom are considered negative activity emotions; lastly, anxiety shame, and hopelessness are considered negative outcome emotions.

2 x 2 Achievement Goal Framework

On the other hand, the first explanatory construct to be considered in this study, which acquires high research attention to predict the predisposition in achievement situations is the achievement goal framework (Elliot, 2005a, 2005b; Berger & Archer, 2016; Kayes, 2005; Elliot & McGregor, 2001). Achievement goals are defined as organized efforts to direct thoughts, actions, and feelings toward the attainment of one’s goals (Sunawan & Xiong, 2016; Thrash & Hurst, 2008; Soini, Aro, & Niemivirta, 2011). And this may predict the use of learning strategies, effort, emotions, and performance (Sanchez-Rosas & Furlan, 2017; Mason, Boscolo, Tornatora, & Ronconi, 2011; Schunk, 2012).

It hypothesized four types of achievement goals namely mastery-approach goal (MAp), mastery-avoidance goal (MAv), performance-approach goal (PAp) and performance-avoidance goal (PAv) (Elliot & Murayama, 2008; Elliot & McGregor, 2001; Elliot, 2005a, 2005b). MAp involves striving to learn all what there is to learn focusing on improving knowledge; MAv involves avoiding failing to learn what there is to learn focusing on preventing misunderstanding or missing any point; PAp involves seeking to perform better than others focusing on demonstrating ability to others and looking smart; and PAv involves avoiding poor performance relative to others focusing on preventing looking slow or getting the worst grades (Murayama, Elliot, & Yamagata, 2011; Elliot & Church, 1997; Elliot & Fryer, 2008; Elliot & Moller, 2003; Elliot & Harackiewicz, 1996; Ignacio & Reyes, 2017).

Individuals holding MAp goals show some kind of confidence and are more academically engaged (Liem, 2016; Shim & Finch, 2014); manifested through positive coping strategies (Pekrun & Perry, 2014; Shimizu, Niiya, & Shigemasu, 2016). Although the pursuit of PAp goals produced mixed results when linked to achievement (Elliot, 2005b; Pajares & Cheong, 2003), however, some researchers have argued that these inconsistent results may just be due to the learning climate (Plante, O’keefe, & Theoret, 2013; Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002; Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000). By viewing failure as a personal challenge, approach-type individuals are assumed to continue trying to reach their goal by strengthening their effort after an initial failure by way of reflecting on abstract concepts and putting the information in logical form (Dickhauser, Buch, & Dickhauser, 2011; Ignacio & Reyes, 2017; Kolb & Kolb, 2005; Elliot & Harackiewicz, 1996).

In contrast, PAv goals have been found to be related only to negative outcomes, that is, low task engagement, and unwillingness to seek help (Tanaka, Okuno, & Yamauchi, 2013; Van Yperen & Janssen, 2002; Mason et al, 2013; Elliot & Church, 1997), similarly, individuals with MAv goals have been found to be related to fear of failure, test anxiety, and other negative emotions (Kahraman & Sungur, 2012; Elliot, 1999). Avoidance-type individuals are most probably trying to avoid further self-blame, which may lead to active avoidance, forcing them devote lesser period of time to operate on favorable learning experiences, resulting to unfavorable achievement situations (Dickhauser, Buch, & Dickhauser, 2011; Elliot & Moller, 2003; Ignacio & Reyes, 2017; Kolb & Kolb, 2005).

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Kolb’s Learning Style Framework

Furthermore, the second explanatory construct to be considered in this study falls under Kolb’s (Kolb & Kolb, 2005; Kolb, 1985, 1984, 1976) experiential learning theory, which postulates the existence of four learning modes that combine to form two bi-polar dimensions: an information-gathering dimension which consists of the union of concrete experience (CE) versus abstract conceptualization (AC), and an information-processing dimension which consists of the union of reflective observation (RO) versus active experimentation (AE). CE consists of using direct experience, feelings and emotions to engage with the world; RO consists of looking back on existing experience, recollecting aspects of the experience and gathering new information about the experience; AC consists of creating sense out of the experience and generating strategies to guide forthcoming actions; and AE consists of testing the plan by putting it into action (Willcoxson & Prosser, 1996; Terry, 2001; Kayes, 2005; Ignacio & Reyes, 2017).

Moreover, the experiential learning theory suggests that individuals tend to develop skills and preferences at using either CE versus AC or RO versus AE. This preference for using certain modes over others is defined as learning style. Each learning style defines characteristics of how individuals learn. Diverger (CE and RO) labels individuals who show a preference for learning through creating, generating new ideas, and imagining possibilities; assimilator (RO and AC) labels individuals who like to learn by drawing from multiple sources of information, logic, and step by step organizing of information; converger (AC and AE) labels individuals who like to learn through solving practical problems, making decisions, and interacting with problems rather than necessarily with people; and accommodator (AE and CE) labels individuals who like to learn through taking actions, risks, and by relying more on others than exploring it (Cornwell & Manfredo, 1994; Di Muro & Terry, 2007).

Consequently, the present researcher hopes that by explaining achievement emotions in mathematics using achievement goal and Kolb’s learning style frameworks, will provide teachers and researchers improved views on mathematics education.

Objective of the Study

The objective of this study is to investigate the effects of achievement goals and Kolb’s learning styles of freshmen teacher education students of Bulacan State University - Extension located in Paltao, Pulilan Bulacan who are currently enrolled in the first semester of school year 2018-2019, on achievement emotions in mathematics.

Specifically, it sought to test whether there is a significant interaction effect between the approach- avoidance dimension of achievement goals and Kolb’s learning styles on achievement emotions in mathematics; to test independently whether there is a significant difference in the means of achievement emotions in mathematics when grouped according to approach-avoidance dimension of achievement goals, and according to Kolb’s learning styles; and lastly, to provide effect-size estimates for each test of significance.

Materials and Methods

Research Design

This study used explanatory cross-sectional design (Creswell, 2014; Burke, 2001). This is a nonexperimental quantitative design used to test an explanatory model or theory about a phenomenon which data are collected at one point in time. Data were collected at a single time point and analyzed for the purpose of hypothesis testing.

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Sampling Technique

This study used cluster sampling (Bluman, 2009). In this sampling technique, the population is divided into groups called clusters, then the researcher randomly selects some of these clusters and uses all members of the selected clusters as subjects. In actual, the researcher utilized three from the four sections of freshmen teacher education students of Bulacan State University - Pulilan Extension located in Paltao, Pulilan, Bulacan who are currently enrolled in the first semester of school year 2018-2019. Specifically, the sections BECEd1-a, BEEd1-a, and BTVTEd1-a were used as samples. Each section was selected in random. The justification behind the selection of freshmen over other year levels is the availability of respondents due to K-12 transition. Likewise, students who were absent, unavailable or unwilling during the conduct of survey were excluded. Thus, from a total of one hundred sixteen (116) officially enrolled students from the three sections involved, this study utilized one hundred five respondents (n = 105).

Instruments

Three research instruments were utilized in this study, specifically, the Achievement Goal Questionnaire (AGQ) by Elliot and Murayama (Elliot & Murayama, 2008; Elliot & McGregor, 2001), Kolb’s Learning Style Inventory 3.1 (KLSI 3.1) (Kolb & Kolb, 2005), and Achievement Emotions Questionnaire for Mathematics (AEQ-M) by Pekrun (Pekrun, Goetz, & Frenzel, 2005b). All instruments used in this study have already long-established validity and reliability measures (Elliot & Murayama, 2008; Elliot & McGregor, 2001; Kolb & Kolb, 2005; Pekrun, Goetz, & Frenzel, 2005b). However, for the purpose of this study, small-scale validity and reliability measures (n = 24) were still observed. Specifically, issues with regards to the items in terms of its Cronbach’s alpha, vocabulary and format were considered.

AEQ is comprised of 12 items, with three items composing each of the four achievement goal orientations. Items are rated on scales ranging from 1 (not at all true of me) to 7 (very true of me). The researcher got evidences indicating its reliability, with , specifically, , , , and . On the other hand, KLSI 3.1 is comprised of 12 rows of sentences with a choice of four endings. Items are in a force-choice format and are rated by ranking the endings of each sentence starting with 4 (most descriptive of you) to 1 (least descriptive of you), with , specifically, and . Lastly, AEQ-M is comprised of 60 items arranged in two dimensions; time and instruction perspectives. 10 items involve enjoyment, 6 for pride, 9 for anger, 15 for anxiety, 8 for shame, 6 for hopelessness, and 6 for boredom. Items are rated on scales ranging from 1 (strongly disagree) to 5 (strongly agree), with , specifically, and

Data Gathering and Processing

In line with this, the researcher first asked permission to conduct research study from the BulSU-Pulilan Extension Head, and after its approval, moved towards the actual survey. On the actual administration of research instruments to respondents, the researcher explained to all respondents all substantial matters regarding the survey along with the observance of confidentiality of personal information. To assure confidentiality, names of the respondents are not required. However, signatures implying their consent were asked. This instruction to respondents were created for the purpose of honesty of reported data, if such related problems will exist.

After the data gathering, the researcher temporarily encoded and sorted all the messy raw data. While and after encoding the raw data, an elimination scheme is strictly observed, in particular, if items in the instruments were found unanswered, or the sum of CE = AC, RO = AE, or an approach-type equals to an avoidance-type for each respondent, then regardless of the percentage of completeness, the response of the students will be excluded in the study. Thus, from the total number of respondents (n = 105), the number of considered valid responses from respondents reduces to ninety-three (n = 93). All considered valid

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I responses were imported to SPSS for further statistical analysis. Particularly, the statistical tools that the researcher utilized to answer specific problems posed in the study are two-way factorial analysis of

variance (two-way ANOVA) (Bluman, 2009) and partial eta-squared (Cohen, 1988). The meaningfulness of results will be discussed in light of effect sizes through the use of partial eta-squared

(Schuele & Justice, 2006).

And as a rule of thumb of Cohen (1988; The Cambridge University, n.d.) when is used in factorial

ANOVA, a negligible effect exists when , a small effect exists when , an

average effect exists when , and a large effect exists when . And in Cohen's (1988) terminology, a small effect size is one in which there is a real effect but which you can only see through careful study. A large effect size, on the other hand, is an effect which is big enough, and/or consistent enough, that an individual may be able to see it with the naked eye. And lastly, an average effect size is one in which is also a real effect, despite the fact that it is somewhat observable, it still requires a particular study.

Results and Discussion

Summary of p-values and partial eta-squared , along with its corresponding interpretation are reported in table 1.

Table 1. Values and Interpretation of p-values ( and Partial eta-squared (

Achievement AG*KLS1 AG2 KLS3 Emotions

4 5 6 I I I I I I Enjoyment .30 NS7 .042 SE8 .01 S9 .087 AE10 .13 NS .064 AE Pride .37 NS .037 SE .03 S .065 AE .07 NS .079 AE Boredom .73 NS .015 SE .28 NS .014 SE .05 S .088 AE Anger .51 NS .027 SE .27 NS .014 SE .03 S .100 AE Anxiety .22 NS .051 SE .19 NS .020 SE .67 NS .018 SE Shame .28 NS .044 SE .23 NS .017 SE .32 NS .040 SE Hopelessness .36 NS .037 SE .14 NS .026 SE .44 NS .031 SE 1 interaction effect of achievement goals and Kolb’s learning styles; 2 achievement goals; 3 Kolb’s learning styles; 4 p-value; 5 interpretation, 6 partial eta-squared; 7 not significant, 8 small effect; 9 significant; 10 average effect.

The Interaction and Effect Sizes

Results show that there is enough evidence to accept the null hypothesis concerning the interaction effect between approach-avoidance dimension of achievement goals and Kolb’s learning styles on achievement emotions in mathematics. That is, there is no significant interaction effect between approach-avoidance

dimension of achievement goals and Kolb’s learning styles on enjoyment , pride

, boredom , anger , anxiety

, shame , and hopelessness .

Likewise, since for all achievement emotions, then there is, undeniably, a small effect on achievement emotions in mathematics. However, the detected small effect brought by the interaction is attributed to chance due to the computed p-values. Hence, there is a real small interaction effect on achievement emotions in mathematics that is attributed to chance which can only be seen through careful study.

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I The Achievement Goals and Effect Sizes

The study produced mixed results concerning the significant difference in the means of achievement emotions in mathematics when grouped according to approach-avoidance dimension of achievement goals. First, results show that there is enough evidence to reject the null hypothesis concerning the approach- avoidance dimension of achievement goals relating to positive achievement-related outcome emotions in

mathematics. That is, there is a significant difference in the means of enjoyment ,

and pride . This implies that approach-avoidance dimension of achievement goals has a significant effect on both enjoyment and pride. Take note that both enjoyment and pride are considered positive achievement-related outcome emotions in the control-value theory of achievement

emotions. Moreover, since both enjoyment and pride produced and , then an average effect exists and is not attributed to chance. In other words, a real effect that is somewhat observable exists on positive achievement-related outcome emotions, not attributed to chance, even though it still requires a particular study.

On the other hand, results also show that there is enough evidence to accept the null hypothesis concerning the approach-avoidance dimension of achievement goals relating to negative achievement-related activity and outcome emotions in mathematics. That is, there is no significant difference in the means of boredom

, anger , anxiety , shame

, and hopelessness . This implies that approach-avoidance dimension of achievement goals has no significant effect on the listed negative achievement-related activity

and outcome emotions. Moreover, since all negative achievement emotions produced and , then just like the interaction effect, there is also a real small effect on negative achievement-related emotions in mathematics that is attributed to chance which can only be seen through careful study.

The Kolb’s Learning Styles and Effect Sizes

The study produced mixed results concerning the significant difference in the means of achievement emotions in mathematics when grouped according to Kolb’s learning styles. First, results show that there is enough evidence to reject the null hypothesis concerning the Kolb’s learning styles relating to negative achievement-related activity emotions in mathematics. That is, there is a significant difference in the means

of boredom , and anger . This implies that Kolb’s learning styles has a significant effect on both boredom and anger. Take note that both boredom and anger are considered negative achievement-related activity emotions in the control-value theory of achievement

emotions. Moreover, since both boredom and anger produced and , then an average effect exists and is not attributed to chance. In other words, a real effect that is somewhat observable exists on negative achievement-related activity emotions, not attributed to chance, even though it still requires a particular study.

On the other hand, results also show that there is enough evidence to accept the null hypothesis concerning the Kolb’s learning styles relating to both positive and negative achievement-related outcome emotions in

mathematics. That is, there is no significant difference in the means of enjoyment ,

pride , anxiety , shame , and

hopelessness . This implies that Kolb’s learning styles has no significant effect on both positive and negative achievement-related outcome emotions. Moreover, since all positive and

negative achievement-related outcome emotions produced , and respectively; and , then there is an average effect on positive achievement-related outcome emotions, and a small effect on negative achievement-related outcome emotions, respectively, both attributed to chance. In other words, although both positive and negative achievement-related outcome

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I emotions are attributed to chance, and that equally, these emotions can be clearly seen through a particular study, only positive achievement-related outcome emotions are somewhat visible in the naked eye.

Conclusion

There is a real small interaction effect between approach-avoidance dimension of achievement goals and Kolb’s learning styles on achievement emotions in mathematics that is attributed to chance which can only be seen through careful study.

A real average effect not attributed to chance, carried by approach-avoidance dimension of achievement goals that is somewhat visible in the naked eye exists on positive achievement-related outcome emotions, namely, enjoyment and pride; even though it still requires a particular study. Likewise, a small effect on negative achievement emotions in mathematics, carried by the same construct, that is attributed to chance can also be seen through careful study.

And lastly, a real average effect not attributed to chance, carried by Kolb’s learning styles that is somewhat visible in the naked eye exists on negative achievement-related activity emotions, namely, boredom and anger; even though it still requires a particular study. Likewise, although both positive and negative achievement-related outcome emotions are attributed to chance, and that equally, these emotions can be clearly seen through a particular study, only positive achievement-related outcome emotions are somewhat visible in the naked eye.

Recommendations

Moderation analysis is recommended to the independent variables with small effect that is attributed to chance; and a mediation analysis to the variables with average effect that is somewhat visible in the naked eye. Another way to think about this recommendation is that a moderator variable is one that influences the strength of a relationship or effect between two other variables, and a mediator variable is one that explains the relationship or effect between the two other variables.

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I https://www.academia.edu/29235455/ACHIEVEMENT_GOALS_AND_MATHEMATICS_PERFOR MANCE_BASED_ON_GENDER Ignacio, A.G. (2016). Students’ viewpoints on mathematics courses in engineering: A basis for improvement. Asia Pacific Journal of Multidisciplinary Research, 4(3), 111–118. Retrieved from http://www.apjmr.com/wp-content/uploads/2016/07/APJMR-2016.4.3.13.pdf Kahraman, N. & Sungur, S. (2012). Antecedents and consequences of middle school students’ achievement goals in science. The Asia-Pacific Education Researcher, 21(3), 535–551. doi: 10.1007/s40299-012- 0024-2 Kayes, C. D. (2005). Internal validity and reliability of Kolb’s learning style inventory version 3 (1999). Journal of Business and Psychology, 20(2), 249–257. doi: 10.1007/s10869-005-8262-4 Kolb, A. & Kolb, D. (2005). The Kolb learning style inventory – version 3.1 technical specifications. Boston: Hay Resources Direct. Retrieved from http://www.whitewaterrescue.com/support/pagepics /lsitechmanual.pdf Kolb, D. (1985). Learning style inventory. Boston: McBer & Co. Kolb, D. (1984). Experiential learning: Experience as the source of learning and development. New York: Prentice-Hall. Retrieved from http://b-ok.cc/book/3508789/beb71f Kolb, D. (1976). Learning style inventory. Boston: McBer & Co. Le, V.N., Stecher, V. M., Lockwood, J. R., Hamilton, L. S., Robyn A., Williams, V. L., Ryan, G., …, Klein, S. P. (2006). Improving mathematics and science education: A longitudinal investigation of the relationship between-oriented instruction and instruction Achievement. CA: RAND Corp. Retrieved from. https://www.rand.org/content/dam/rand/pubs/monographs/2006/RAND_MG480.pdf Liem, G.D. (2016). Academic and social achievement goals: Their additive, interactive, and specialized effects on school functioning. British Journal of Educational Psychology, 86, 37–56. doi: 10.1111/bjep.12085 Linnenbrink-Garcia, L., & Pekrun, R. (2011). Students’ emotions and academic engagement: Introduction to the special issue. Contemporary Educational Psychology, 36,1–3. doi: 10.1016/j.cedpsych.2010.11.004 Mason, L., Boscolo, P., Tornatora, M. C., & Ronconi, L. (2013). Besides knowledge: a cross-sectional study on the relations between epistemic beliefs, achievement goals, self-beliefs, and achievement in science. Instr Sci, 41, 49–79. doi: 10.1007/s11251-012-9210-0 Murayama, K., Elliot, A. J., & Yamagata, S. (2011). Separation of performance-approach and performance- avoidance achievement goals: A broader analysis. Journal of Educational Psychology, 103, 238–256. doi: 10.1037/a0021948 Pajares, F., & Cheong, Y. F. (2003). Achievement goal orientations in writing: A developmental perspective. International Journal of Educational Research, 39(4), 437–455. doi: 10.1016/j.ijer.2004.06.008 Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In A. Alexander, R. Pekrun & L. Linnenbrink-Garcia (Eds.), Handbook of emotions in education (pp. 120–141). New York: Taylor & Francis. doi: 10.4324/9780203148211.ch7 Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48. doi: 10.1016/j.cedpsych.2010.10.002 Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educ Psychol Rev, 18, 315–341. doi: 10.1007/s10648-006-9029-9 Pekrun, R., Elliot, A. J., & Maier, M. A. (2006). Achievement goals and discrete achievement emotions: A theoretical model and prospective test. Journal of Educational Psychology, 98, 583–597. doi: 10.1037/0022-0663.98.3.583 Pekrun, R., Goetz, T., & Frenzel, A. C. (2005a). Achievement emotions questionnaire (AEQ). User’s manual. Department of Psychology, University of Munich, Germany. Pekrun, R., Goetz, T., & Frenzel, A. C. (2005b). Achievement emotions questionnaire - mathematics (AEQ- M). User’s manual. Department of Psychology, University of Munich, Germany.

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002a). Academic emotions in students’ self-regulated learning and achievement: A program of quantitative and qualitative research. Educational Psychologist, 37, 91–106. Pekrun, R., Goetz, T., Titz, W, & Perry, R. P. (2002b). Positive emotions in education. In E. Frydenberg (Ed.), Beyond coping: Meeting goals, visions, and challenges (pp. 149–174). UK: Elsevier. doi: 10.1093/med:psych/9780198508144.001.0001 Plante, I., O’keefe, P., & Theoret, M. (2013). The relation between achievement goal and expectancy-value theories in predicting achievement-related outcomes: A test of four theoretical conception. Motiv Emot, 37,65–78. doi: 10.1007/s11031-012-9282-9 Sanchez•Rosas, J., & Furlan, L. A. (2017). Achievement emotions and achievement goals in support of the convergent, divergent and criterion validity of the Spanish •cognitive test anxiety scale. International Journal of Educational Psychology, 6(1), 67–92. doi:10.17583/ijep.2017.2268 Schuele, M. & Justice, L. (2006). The importance of effect sizes in the interpretation of research. The ASHA Leader 11, 14–27. doi: 10.1044/leader.FTR4.11102006.14 Schunk, D. H. (2012). Learning theories: An educational perspective. Boston: Pearson. Retrieved from http://b-ok.cc/book/1236789/15cb12 Shim, S. S., & Finch, W. F. (2014). Academic and social achievement goals and early adolescents’ adjustment: A latent class approach. Learning and Individual Differences, 30, 98–105. doi: 10.1016/j.lindif.2013.10.015 Shimizu, M., Niiya, Y., & Shigemasu, E. (2016). Achievement goals and improvement following failure: moderating roles of self-compassion and contingency of self-worth. Self and Identity, 15(1), 107–115. doi: 10.1080/15298868.2015.1084371 Skovsmose, O. (2016). Mathematics: A critical rationality. In P. Earnest, B. Sriraman, & N. Earnest (Eds.), Critical mathematics education: Theory, praxis, and reality (pp. 1–22). USA: Information Age Publishing Inc. Retrieved from http://b-ok.cc/book/2876454/5d2512 Skovsmose, O. (2011). An invitation to critical mathematics education. The Netherlands: SensePublishers. Retrieved from http://b-ok.cc/book/1259539/8bccd6 Soini H., Aro, K. & Niemivirta, M. (2011). Stability and change in achievement goal orientations: A person centered approach. Contemporary Educational Psychology, 36, 82 – 100. Retrieved from https://doi.org/10.1016/j.cedpsych.2010.08.002 Sriraman, B. (2016). Critical mathematics education: Cliché, dogma, or commodity. In P. Earnest, B. Sriraman, & N. Earnest (Eds.), Critical mathematics education: Theory, praxis, and reality (pp. ix– xii). USA: Information Age Publishing Inc. Retrieved from http://b-ok.cc/book/2876454/5d2512 Sunawan, S. & Xiong, J (2016). An application model of reality therapy to develop effective achievement goals in tier three intervention. International Education Studies, 9(10), 16–26. doi:10.5539/ies.v9n10p16 Tanaka, A., Okuno, T., & Yamauchi, H. (2013). Longitudinal tests on the influence of achievement goals on effort and intrinsic interest in the workplace. Motiv Emot, 37,457–464. doi: 10.1007/s11031-012- 93181 The Cambridge University Website. Retrieved from http://imaging.mrc-cbu.cam.ac.uk/statswiki/FAQ /effectSize Thrash, T. M., & Hurst, A. L. (2008). Approach and avoidance motivation in the achievement domain: Integrating the achievement motive and achievement goal tradition. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp. 217–233). New York, NY: Psychology Press. Retrieved from http://b-ok.cc/book/1084203/6d4379 Terry, M. (2001) Translating learning style theory into university teaching practices: An article based on Kolb's experiential learning model. Journal of College Reading and Learning, 32(1), 68–85. doi: 10.1080/10790195.2001.10850128 Van Yperen, N. W., & Janssen, O. (2002). Fatigued and dissatisfied or fatigued but satisfied? Goal orientations and responses to high job demands. Academy of Management Journal, 45, 1161–1171. doi: 10.5465/3069431

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R S www.irss.academyirmbr.com February 2019 S International Review of Social Sciences Vol. 7 Issue.2 I Willcoxson, L. & Prosser, M. (1996). Kolb’s learning style inventory (1985): Review and further study of validity and reliability. British Journal of Educational Psychology, 66, 247–257. doi: 10.1111/j.2044- 8279.1996.tb01193.x Zeidner, M. (2007). Test anxiety in educational contexts: What I have learned so far. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 165–184). CA: Academic Press. Retrieved from http://b- ok.cc/book/2073493/c597c0 Zhao, D. (2016). Chinese students’ higher achievement in mathematics: Comparison of mathematics education of Australian and Chinese primary schools. Singapore: Springer Nature. Retrieved from http://b-ok.cc/book/2679609/091413.

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