ACADEMIC AND DISCIPLINARY OUTCOMES FOLLOWING ADJUDICATION OF ACADEMIC DISHONESTY
Casey K. Sacks
A Dissertation
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
May 2008
Committee:
Michael Dannells, Advisor
Julie Lengfelder Graduate Faculty Representative
Robert DeBard
William Knight
© 2008
Casey Sacks
All Rights Reserved iii
ABSTRACT
Michael Dannells, Advisor
Academic disciplinary processes are viewed by the academic community as the
university’s attempt to communicate expectations about honest scholarly behavior. Most
institutions have some process in place, but empirical evidence about the relationship
between disciplinary processes and later student performance was lacking. This study
investigated the relationship of a collegiate disciplinary process experience on
subsequent academic performance by examining student records. A profile of students
who were reported for academic dishonesty is presented. Findings indicated sanctions did not impact student retention to the semester following adjudication or student GPA following adjudication. There were, however, students in some subpopulations who were at greater risk for attrition from the university and at risk of attaining lower GPAs following adjudication. There were also differences in who was reported for academic dishonesty than would have been expected given student self-reported numbers from previous research. Implications are discussed. iv
This work is dedicated to my best friend from college. Thank you for cheating, thank you
for getting caught. I would never have thought to do this project if it wasn’t for you. v
ACKNOWLEDGMENTS
There are so many people who helped me to make this work a reality. Thank you.
Mike, my dissertation advisor, who pushed me to become a better writer and scholar, you are the best I could have asked for. I really appreciate that you are always willing to chat about unrelated topics because we have the luxury to think and discuss ideas here. You have been an exceptional advisor, mentor, and friend.
The members of my dissertation committee: Bill, Bob, and Julie, for their support throughout the process and making this final product better. Bill, there is no way I could have collected this data without you. You have no idea how amazing you are. Bob, you have been one of my greatest cheerleaders; you have the biggest heart of anyone I know.
Julie, you have believed in me from the beginning.
Thank you to the HIED program faculty for providing me with an excellent education and experience here at BGSU. Nicole, Brady, Sally, and Gina: my dear friends and cohort members for sharing meals, jokes, thoughts, and encouragement through this process.
My parents, for believing in me, for being excited for me, for crying with me, and for celebrating with me; I love you. You are an incredible support system and even more incredible parents. Ditto and Alligator.
Erik, my wonderful fiancé, for being so supportive, for the countless airlines tickets, visits, and phone calls; and for unconditionally loving and supporting me through my education and in my life. I learn so much from you everyday, I love you. vi
TABLE OF CONTENTS
Page
CHAPTER I. INTRODUCTION AND PURPOSE OF THE STUDY...... 1
Research Questions ...... 2
Significance of Study...... 3
CHAPTER II. LITERATURE REVIEW...... 5
Prevalence of Cheating ...... 7
Determinants of Academic Dishonesty ...... 8
Student Demographics (Inputs)...... 8
Environments ...... 12
Problems with Current Measures of Cheating...... 15
Who is Caught Cheating?...... 17
Need for this Study...... 18
CHAPTER III. METHODS…………………………………………………………...... 19
Research Problem...... 19
BGSU Academic Honesty Policy Rules and Guidelines...... 19
Research Questions...... 21
Outcomes Following Academic Dishonesty...... 22
Descriptive Demographic Profile ...... 23
Research Design...... 24
Procedures and Data Collection...... 24
The Use of Effect...... 25
Limitations...... 25 vii
CHAPTER IV. RESULTS...... 28
Descriptive Demographic Profile of Students Reported for Academic Dishonesty.. 31
Group Differences: Retention, GPA, Charges and Penalties...... 37
Gender...... 37
Academic Preparedness...... 40
Testing into Developmental Coursework ...... 43
Student GPA before Adjudication ...... 46
Age at Time of Academic Dishonesty...... 49
Student Status ...... 52
International Students ...... 55
Student Athletes...... 58
Academic Colleges ...... 61
Regression Analyses...... 64
Summary of Statistically Significant Findings ...... 68
CHAPTER V. DISCUSSION...... 70
Findings………………...... 70
Discussion...... 72
Limitations Revisited...... 78
Suggestions for Future Research ...... 78
Implications and Recommendations...... 80
Conclusion ...... 82
REFERENCES ...... 83 viii
LIST OF TABLES
Table Page
1 Descriptive Statistics: Comparison of Academic
Dishonesty Population to University Population...... 28
2 GPA Ranges Before Adjudication...... 31
3 Student Age at the Time of Academic Dishonesty:
Comparison of Academic Dishonesty Population to University Population...... 32
4 Types of Charges Filed ...... 33
5 Penalties Imposed Following Adjudication...... 34
6 Combined Penalties Following Adjudication...... 35
7 Student Retention Following Adjudication ...... 36
8 GPA Following Adjudication ...... 36
9 Charge by Gender ...... 38
10 Gender and Penalty Assigned...... 39
11 ACT Group by Charge...... 41
12 Penalty Imposed and ACT Groups ...... 42
13 Testing into Developmental Coursework and Charge...... 44
14 Testing into Developmental Coursework
and Penalty for Academic Dishonesty...... 45
15 High or Low GPA Group and Charge ...... 47
16 High or Low GPA Group and Penalty...... 48
17 Descriptive Data About Each Age Group
and GPA Following Adjudication ...... 49 ix
18 Age of Student at Time of Adjudication and Charge ...... 50
19 Penalty and Age of Student at Time of Adjudication...... 51
20 Charge by Graduate or Undergraduate Student Status ...... 53
21 Penalty and Student Status...... 54
22 Charge and International Student Status...... 56
23 Penalty and International Student Status ...... 57
24 Charge and Student Athlete Status ...... 59
25 Penalty and Student Athlete Status...... 60
26 Charge and College...... 62
27 Penalty and College ...... 63
28 Summary of Logistic Regression Analysis Predicting Student
Retention Following Adjudication of Academic Dishonesty...... 65
29 Summary of Linear Regression Analysis Predicting Student GPA
Following Adjudication of Academic Dishonesty...... 66
30 Summary of Logistic Regression Analysis Predicting
Academic Dishonesty Adjudication from Race...... 68 Academic 1
CHAPTER I: INTRODUCTION AND PURPOSE OF THE STUDY
By some estimates, as many as 80% of students enrolled in college have committed some act of academic dishonesty during their college careers (Callaway, 1998; Marsden, Carroll, &
Neill, 2005). This figure is more frequently estimated to be in the neighborhood of 70% of all
college students (Roig & Caso, 2005; Whitley, 1998). By either estimate, many students on
campuses nationwide are finding ways to cheat in their academic endeavors.
Academic honesty is one of the foundations of institutions of higher education. Bowers
(1964) suggested “cheating, plagiarism, and other forms of academic dishonesty on the college
campus run contrary to the fundamental values underlying the institution of higher education in
America” (p. 1). A college education is not about memorizing something to recite for an exam; institutions of higher education across the country espouse values that include moral and character development for individual students through their educational experience. Pavela
(2007) said an important distinction between colleges and businesses is that colleges have a
mission that is about “truth-seeking and character formation” (p. 1). Schwartz (2000) advocated
for higher education to “establish character development as a high institutional priority” (p.
A68).
Outcomes research has been a weakness in all areas of student discipline. Academic
dishonesty is no exception; there is no research examining academic outcomes for students after
adjudication of academic dishonesty.
Over three hundred books and articles have examined student characteristics and
environments that contribute to cheating (Bennett, 2005; Brown & Emmett, 2001; Whitley,
1998). Most of this research is based on self-report, not on information of students who have
actually been found engaging in academic dishonesty. This has led researchers to call for more Academic 2 direct measures of academic dishonesty (Barnett & Dalton, 1981; Bolin, 2004; Callaway, 1998).
There is knowledge of a general demographic profile of a cheating student, but there are no data to date to describe students who have been found cheating. And more importantly, there are no data to identify if there are any similarities or differences between students who are caught cheating and other students on campus.
Self-reported data do not provide a picture of what happens once students are found cheating. Further, what happens once these students are adjudicated in a university disciplinary system has never been formally assessed. After an exhaustive literature review only one article,
Whitley (2001), was found to examine student outcomes following academic dishonesty. In this
case Whitley researched affective outcomes, not academic outcomes.
This study examined records of students who had been found in violation of a single
institution’s academic honesty policy; examining input, environmental, and outcome measures
over four years. The purpose of this study was to measure academic outcomes after academic
misconduct was adjudicated by using institutional data specific to Bowling Green State
University (BGSU). Details describing the BGSU policy and guidelines are described in the
methods section of this paper.
Research Questions
The primary research question for this project was: What are the academic outcomes for
students who have been held accountable for academic dishonesty? Academic outcomes
measured included semester GPA following adjudication of academic dishonesty and retention
to the university following adjudication. The secondary outcomes assessed in this project were
disciplinary outcomes following academic dishonesty. Disciplinary outcomes included a
warning, partial credit on an assignment or exam, no credit on an assignment or exam, a failing Academic 3 grade in the course, a grade of ‘WF’ (withdraw-fail in the course) recorded on the transcript, and removal from campus by way of suspension, dismissal, or expulsion. These outcomes are the range of possible penalties for offenses of academic dishonesty at BGSU.
The second research question for this project was: What are the demographic profiles of
academically dishonest students who are reported? This was measured by examining institutional
research data on gender, cumulative college GPA before the incident, age at the time of the
academic dishonesty incident, ACT score, if the student tested into developmental course work,
international student status, undergraduate/graduate student status, involvement (measured by
intercollegiate athletic status), and college (where the student declared their major). The third
research question for this project was: How do students who are reported for academic dishonesty compare to their peers in the university? Comparisons were made with regard to demographic profiles and academic performance.
Significance of Study
This study made a significant contribution to our knowledge about academic dishonesty in several ways. Researchers, instructors, and administrators need more tools to evaluate disciplinary processes for students who are involved in cases of academic misconduct (Kibler,
1993). Through academic outcomes assessment, disciplinary outcomes assessment, and the creation of a demographic profile of students who were actually found cheating, this project concretely assessed adjudication of academic misconduct.
Discovering there are differences in groups that cheat was not new information (e.g., students with lower GPAs cheat more). However, discovering there were different outcomes for different populations is information that until this point did not exist. Administrators need to understand outcomes following academic misconduct because part of their role is to engage in Academic 4 discussions about what should happen with students following misconduct. Understanding academic outcomes will allow a more complete discussion of an academic honesty process at an institution and stimulate conversations about what outcomes should look like for students on a given campus. Once students are found to have engaged in academic dishonesty, are institutions educating those students about appropriate future academic conduct? Are those students foundering in the educational system? Are they continuing on at the institution?
A demographic profile allows future researchers to ask questions about differential treatment for students in the disciplinary process. For example, research has suggested men and women may be equally likely to cheat overall. However, because men take more risks with cheating, they may be more likely to be caught (Do Men Plagiarize More?, 2004). We do not know if the demographic profile of students who are found by their instructors to be engaging in academic dishonesty is a similar profile to students who admit to cheating.
This study used institutional data and provided more reliable measures than self-report has allowed in previous research. Examining outcomes from actual academic dishonesty cases allowed a more detailed description of students who were referred to a formal process.
Academic honesty is a fundamental value in higher education. This research addressed questions that have never been addressed before with regard to outcomes related to academic dishonesty. This study examined both academic and disciplinary outcomes of students who were reported for violating an institution’s academic honesty policy. Academic 5
CHAPTER II: LITERATURE REVIEW
This review covers outcomes research in current academic dishonesty literature, prevalence of student cheating, determinants of academic dishonesty, problems with current studies, who is caught engaging in academic dishonesty in college, and the need for this research.
There are two seminal studies on college students’ academic dishonesty, Bowers (1964) and
McCabe and Trevino (1993). Both focused on large samples of students at several institutions to measure prevalence and perceptions of academic dishonesty.
Bowers (1964) surveyed the general student population, student body presidents, and
deans at 99 institutions of higher education to determine perceptions of academic dishonesty.
While the entire scope of his project is too extensive to cover here, Bowers aimed to answer
questions regarding how much academic dishonesty occurs on campuses, what causes students to
cheat, and how to reduce cases of academic dishonesty. He reported the number of students who cheated on exams, on homework, and who plagiarized. He found peer approval and the university academic environment had the strongest relationships with student cheating.
Building directly on Bowers’ work, McCabe and Trevino (1993) surveyed just over 6,000 students at 31 small, highly selective institutions. Cheaters were defined as anyone answering affirmatively to any of the 12 types of dishonesty identified by the researchers. The types of cheating evaluated ranged from plagiarism to copying from another student’s exam. They found students from different academic fields cheated at different rates, citing business students as cheating the most. Overall, they reported 75% of all students engaged in some type of academic dishonesty in college.
In 1994 McCabe and Bowers collaborated to examine academic dishonesty across time.
They compared the 1964 and 1993 studies for overlap in the two samples. They limited their Academic 6 comparison sample by only including men and looked only at definitions of cheating that were common to both studies. The students in the McCabe sample in the 1990s were significantly less likely to report cheating than the students in the Bowers sample. For the institutions that participated in both projects findings included the level of unpermitted collaboration (a particular type of cheating) increased in the 30-year period and all other reported academic dishonesty
appeared to be stable over time.
While student demographics and environmental influences related to academic
dishonesty have been extensively studied, only one study addressed students after a cheating
incident. Whitley (2001) examined affective response of students at a single institution after
cheating occurred. Students self-defined cheating by responding affirmatively to questions that
asked if they had cheated on an exam in the past six months. Whitley was interested in students’
attitudes about their own cheating after an actual incident. One hundred seventy students (92
men and 78 women), who were part of a larger sample who participated in survey research to
fulfill a course requirement, who had cheated on an exam responded. The participants completed
a series of questionnaires that assessed attitudes on cheating and students’ affective response to
their own academic dishonesty. Their affective responses were measured on a positive/negative
affect scale. They were asked to think back to a time when they cheated and to think about how
that made them feel.
Whitley (2001) found there were gender differences in affective response to cheating;
“men had a more positive affective response and women had a more negative affective response”
(p. 255). Women were more likely to report feeling uncomfortable, nervous, and guilty about
their cheating, while men were more likely to report they were comfortable with their cheating Academic 7 behavior. No other affective response differences were noted and Whitley suggested researching students after cheating on variables other than affective response.
The paucity of research examining outcomes is strange considering outcomes are usually the first thing that is measured with regard to any phenomena (Astin, 1991). Academic outcomes are a function of adjudication of cases of academic dishonesty because consequences from adjudication are usually academic sanctions (impacting GPA, credit hours earned, and prerequisites for other courses). These sanctions influence academic outcomes that need to be measured and have not been researched to this point. What we do know about demographics and
environments related to academic dishonesty is described below.
Prevalence of Cheating
Academic dishonesty is a phenomenon that has been studied in many countries throughout the world (Bates, Davies, Murphy, & Bone, 2005; Buranen & Roy, 1999; Burns,
Davis, Hoshino, & Miller, 1998; Diekhoff, LeBeff, Shinohara, & Yasukawa, 1999; Evans &
Youmans, 2000; Gitanjali, 2004; Marsden, Carroll, & Neill, 2005; Noah & Eckstein, 2001; Park,
2003; Rawwas, Swaidan, & Isakson, 2007). Researchers have consistently tried to identify how pervasive cheating is in various student populations.
Whitley (1998) completed a meta analysis designed to examine 107 studies on academic dishonesty that were conducted between 1970-1996. He identified factors correlated with cheating and found “student characteristics, attitudes, personality, and situational variables” (p.
236) as being the most important in research studies addressing academic dishonesty. He assessed prevalence of cheating overall with the caveat that studies differently assessed what constituted cheating. He estimated total cheating to be approximately 70% of a given student population: cheating on tests at 43%, cheating on homework 41%, and plagiarism at 47%. Academic 8
Since Whitley’s meta analysis, several more studies have reported overall student cheating rates in their samples (Blakenship & Whitely, 2000; Callaway, 1998; Dawkins, 2004;
Finn, 2001; Pino & Smith, 2003; Mustaine & Tewksbury, 2005; Robinson, Amburgey, Swank,
& Faulker, 2004; Zelna & Bresciani, 2004). While all of these studies have reported different prevalence rates in their populations, the prevailing thought seems to be that about 70% of students have cheated at some point in their academic careers.
Determinants of Academic Dishonesty
Several variables have been identified as potential causes of student academic dishonesty including academic ability, demographics, personality, attitudes, perceptions of cheating, and
morality.
Student Demographics (Inputs)
Most research about academic dishonesty is descriptive and focuses on demographic or input characteristics of self-reported cheaters. Inputs help define who students are before they
begin their college experience and the demographics of gender, academic ability (GPA,
precollegiate academic performance), age, international student status, and socioeconomic status
have been explored. Astin (1991) said inputs must be studied because “inputs are always related
to outputs, and because inputs are related to environments” (p. 64).
Gender
Most researchers agree there is a significant difference between men and women and
dishonest conduct, with men reporting that they cheat more frequently than women (Cizek, 1999;
Cizek, 2003; Dawkins, 2004; Gocial, 1989; Hendershott, Drinan, & Cross, 1999; Iyer &
Eastman, 2006; Marsden, Carroll, & Neill, 2005; McCabe & Trevino, 1997; Mustaine &
Tewksbury, 2005; Pino & Smith, 2003; Roig & Caso, 2005). Academic 9
While Jendrek (1992) did not report differences in actual occurrence of academic dishonesty between men and women, he found men were more likely than women to observe cheating, ignore cheating, express indifference to others cheating, be asked to help someone else cheat, be willing to help someone else cheat, think academic dishonesty was not a problem, say academic dishonesty is justified, and say reporting cheating is as bad as cheating.
(p. 271)
The idea that men cheat more than women has not been universally supported. In a review of 48 studies focused on gender differences in academic dishonesty Whitley, Nelson, and
Jones (1999) found that attitudes are different about cheating (women think it is worse), but behavior was similar in both groups. McCabe (2001) noted the gender differences once observed seem to be “eroding over time” (p. 41); men and women are beginning to look more similar on measures of academic dishonesty. Crown and Spiller (1998) said their finding of a
“nonsignificant relationship between gender and cheating during the last 10 years might suggest a convergence in role requirements among males and females in collegiate settings” (p. 685).
Men and women may be equally likely to cheat overall but because men take more risks with
cheating they may be more likely to be caught (Do Men Plagiarize More?, 2004).
Academic Ability
Grade point average. Drake (1941) concluded “cheating tends to be more prevalent
among the less intelligent students” (p. 419). This finding is consistent with studies that have
included GPA as an input variable assuming GPA is equated to intelligence (Bunn, Caudill, &
Gropper, 1992; Bushway & Nash, 1977; Cizek, 2003; Finn, 2001; McCabe, 2001; Pino & Smith,
2003; Roig & Caso, 2005; Roig & DeTommaso, 1995; Scheers & Dayton, 1987; Whitley, 1998).
Cizek (1999) indicated academic achievement as measured by GPA is “one of the most Academic 10 consistent among the demographic variables” (p. 95) at predicting cheating behavior. When describing why poorer performing students are more likely to cheat McCabe and Trevino (1997) said poorer performing students “have more to gain and less to lose” (p. 381).
Wright and Kelly (1974) noted several differences in observed cheating between high and low GPA groups. Higher GPA students were less aware of cheating in their environments. They found only 3% of students overall knew people who were caught cheating, but 12% of the low
GPA group knew people who were caught cheating. Their findings illustrated differences between low and high performing students.
Pre-college academic performance. Student college entrance exam score (ACT or SAT) as well as testing into developmental course work are believed to be indicators of academic ability before enrolling in college. While testing into developmental course work has not been previously examined with regard to cheating behavior, ACT and SAT scores have. Students who report cheating have been found to have lower SAT or ACT scores (Kelly & Worell, 1978;
McCabe, 1992; Wilkinson, 1974). It has been previously hypothesized that students might cheat because they are not prepared for college level work (Mustaine & Tewksbury, 2005). Daniel,
Blount, and Ferrell (1991) noted students of lesser academic ability were more likely to cheat in college.
Age
Both chronological age and undergraduate class standing have been included as demographic variables of interest in a number of studies about academic dishonesty. It is generally believed that age, rather than class standing, is the significant indicator of who is likely to cheat, with younger students being more likely to engage in academic dishonesty than older students (Callaway, 1998; Cizek, 2003; Crown & Spiller, 1998; Dawkins, 2004; Iyer & Eastman, Academic 11
2006; McCabe & Trevino, 1997; Pino & Smith, 2003; Whitley, 1998). Crown and Spiller (1998) noted age “effects are difficult to detect [because] in most studies age is restricted to a five-year span” (p. 689).
International Students
National origin has been studied by several authors with regard to academic dishonesty
(Buranen & Roy, 1999; Burnett, 2002; Burns, Davis, Hoshino, & Miller, 1998; Evans &
Youmans, 2000; Lin & Wen, 2007; Lupton & Chapman, 2002; Magnus, Polterovich, Danilov, &
Savvateev, 2002; Marsden, Carroll, & Neill, 2005; Park, 2003). All seem to agree that
international students should be examined as a separate group when considering academic
dishonesty because their cultural traditions may dictate different academic behavior than is
required in the United States. Whitley and Keith-Spiegel (2001) said “international students may
not be aware of what behaviors the European cultural tradition defines as academic dishonesty”
(p. 335).
Attitudes and Personality
While not specifically explored in this research, it is worth noting that attitude (Bennett,
2005), impulsivity (Angell, 2004), personality (Nathanson, Paulhus, & Williams, 2006), risk
taking (Blakenship & Whitely, 2000; Mustaine & Tewksbury, 2005), and deviant behavior
(Bolin, 2004) have been found to be related to academic dishonesty.
Summary of Inputs
Generally, men reported cheating more than women, but this appears to be changing over
time as men and women have started to report similar levels of academic dishonesty. Younger
students and students who are not doing as well academically also reported cheating more often than older students and students who are doing well academically. While not known if they cheat Academic 12 at different rates overall, international students should be considered separately from domestic students when evaluating academic dishonesty.
Environments
Astin (1991) noted “environments encompass everything that happens to a student during the course of an educational program that might conceivably influence the outcomes under consideration” (p. 81). Policies (Crown & Spiller, 1998), student major (Bennett, 2005), honor codes (Crown & Spiller, 1998), involvement (Bennett, 2005; Crown & Spiller, 1998), and instructor (Bennett, 2005) have all been found to be environmental variables that impact academic dishonesty. Jendrek (1992) noted environmental factors are likely the most widely studied with regard to academic dishonesty because institutions can alter the “situational or environmental factors that affect cheating” (p. 272).
Policies
Policies in general do not seem to serve as a deterrent to academic dishonesty (Callaway,
1998; Cochran, Chamlin, Wood, & Sellers, 1999; Cole & McCabe, 1996; Wajda-Johnston,
Handal, Brawer, & Fabricatore, 2001). Wajda-Johnston et al. (2001) pointed out cheating behavior is contextual; it is not based on understanding institutional policies. They said
“knowledge of the institutional policy regarding academic honesty or integrity does not deter the rate of academically dishonest behavior” (p. 302). However fear of punishment for academic dishonesty may deter the behavior (Eskridge & Ames, 1993; Leming, 1980).
Davis and Ludvigson (1995) examined the occurrence, reasons, and influence of penalties on academic dishonesty in a sample of over 2,000 undergraduates in 71 classes in 11 states. (They did not report the number of institutions that participated in the project.) They found women cheated less than men when penalties were announced in classes. Academic 13
Academic Major
Several studies have linked academic major to cheating (Bates, Davies, Murphy, &
Bone, 2005; Bowers, 1964; Iyer & Eastman, 2006; Marsden, Carroll, & Neill, 2005; McCabe &
Trevino, 1993). There is not agreement about which majors students cheat in more frequently.
Bowers (1964) and McCabe and Trevino (1993) reported business students cheat the most. But
Iyer and Eastman (2006) found business students were less likely to cheat than other majors.
Bates, Davies, Murphy, and Bone (2005) found pharmacy students cheated the most. Still
Marsden, Carroll, and Neill (2005) found engineering students more likely to cheat than other majors, but when other demographic factors were controlled for, science and journalism students cheated the most. There does seem to be agreement that there are differences between students in different majors with regard to prevalence of academic dishonesty. There has been some speculation that more academic dishonesty takes place in majors where there is a right answer on exams and in majors where there is less interaction with faculty members on any given assignment (Pullen, Ortloff, Casey, & Payne, 2000).
Honor Codes
McCabe and Trevino (1993) examined academic honor codes specifically as an environmental variable that had the potential to be manipulated on campuses. It has been reported students at institutions with an honor code in place were less likely to cheat than students at non-code institutions (McCabe, 2005; McCabe & Trevino, 1993; McCabe, Trevino,
& Butterfield, 1999; McCabe, Trevino, & Butterfield, 2001b; McCabe, Trevino, & Butterfield,
2002). But Gallant and Drinan (2006) said there is an “overemphasis on honor codes” (p. 856) and identified honor codes as a “blind alley” (p. 856).
Student Involvement Academic 14
Involvement has been identified as one of the largest contributing factors when trying to predict academic dishonesty (Whitley, 1998). Cochran, Chamlin, Wood, and Sellers (1999) examined peer influence and found it explained a significant amount of the variance with regard to academic dishonesty. Pino and Smith (2003) found participants in organizations on campus were more likely to cheat than students who were not members of a student organization. Student athletes have been found to cheat more than non-athletes (Diekhoff, LeBeff, Clark, Williams,
Francis, & Haines, 1996; Haines, Diekhoff, LaBeff, & Clark, 1986; McCabe & Trevino, 1996,
1997).
Faculty and Instructor Influence
Instructor influence is a powerful environmental variable with regard to academic dishonesty. Individual instructors have the ability to structure courses and build relationships with students that discourage dishonest behavior (Genereux & McLeod, 1995; Lathrop & Foss,
2005; Levy & Rakovski, 2006; McCabe & Trevino, 1996; McCabe, Trevino, & Butterfield,
1999; Marcoux, 2002; Murdock, Hale, & Weber, 2001; Zelna & Bresciani, 2004).
Faculty members have varied reactions to academic dishonesty, from not being aware of policies (McCabe & Trevino, 1997), being lax in enforcement of those policies (McCabe &
Drinan, 1999; McCabe & Trevino, 1997; McCabe, Trevino, & Butterfield, 2001b; Robinson-
Zanartu, Penam, Cook-Morales, Pena, Afshani, Nguyen, 2005; Vines, 1996; Williams & Hosek,
2003), to deciding not to report cases because it takes too much time (Aurer & Krupar, 2001;
Nuss, 1984; Vines, 1996; Wright & Kelly, 1974). It seems clear that not every case of academic dishonesty found out by a faculty member is addressed. Faculty members describe policies as being too bureaucratic (Keith-Spiegel, Tabachnik, Whitley, & Washburn, 1998; McCabe &
Drinan, 1999; Schneider, 1999), and decisions that are made about academic dishonesty are not Academic 15 made with institutional policies in mind (Jendrek, 1989; Saddlemire, 2005; Schneider, 1999).
Rather, many cases of academic dishonesty are handled in a case-by-case fashion (Jendrek,
1989; Pino & Smith, 2003; Schneider, 1999; Vines, 1996).
Despite all this, some student behavior is still addressed through proper institutional channels. McCabe, Trevino, and Butterfield (2001a) found 32% of faculty members reported their suspicions of student cheating when 88% suspected something. McCabe, Trevino, and
Butterfield reported only more serious infractions were referred to the university level disciplinary system. Hard, Conway, and Moran (2006) found the more faculty knew and understood institutional policy, the more likely they were to try to prevent and confront misconduct.
Problems with Current Measures of Cheating
There are several problems with current measures used in a number of studies assessing academic dishonesty including definitions, self-reported behavior, questionnaire techniques, different behaviors measured as a single construct, and institutional context. Academic dishonesty is not clearly defined in research or in academic settings. The concept of what is appropriate and not appropriate behavior is contextual and depends on the individual interpreting the behavior in question. Defining academic honesty may be similar to the Supreme Court’s definition of pornography, you may not be able to define it, but you know it when you see it
(Noah & Eckstein, 2001). Researcher definitions of academic dishonesty are not always consistent with faculty or student definitions (Ashworth, Bannister, & Thorne, 1997; Eskridge &
Ames, 1993; Higbee & Thomas, 2002; Klein, 2005; Passow, Mayhew, Finelli, Harding, &
Carpenter, 2006; Stephens, 2004; Stover & Kelly, 2005). Academic 16
Further, researchers do not agree on behaviors that should be identified as cheating
(Stephens, 2004), and researchers use different behaviors to define dishonesty (Bowers, 1964;
Mardson, Carroll, & Neill, 2005; McCabe, 1992; McCabe & Bowers, 1994; McCabe & Bowers,
1996; McCabe & Trevino, 1993). Items that are included in one questionnaire are not identified
as cheating in subsequent studies. As Stover and Kelly (2005) noted, some surveys measure
behavior that may not be cheating; for example, asking students about using writing tutors’
assistance (Brown, 1995) or “having someone else check over a paper before turning it in”
(Brown, 1995, p. 153).
The lack of a common definition of academic dishonesty has made self-reported cheating
problematic (Higbee & Thomas, 2002). Students and faculty have indicated behavior is
contextual and it depends on the situation as to whether or not a behavior is dishonesty. Articles
that examine prevalence of cheating look at self-report in a forced-choice format for students that
does not provide a context for the behavior. This may artificially inflate predictions about how
many students are cheating in college.
Self-reported measures of demographic variables related to academic dishonesty have
also been problematic. Studies examining student GPA, ACT scores, or socioeconomic status
(SES) have had to rely on students’ memories and best guesses. Some authors have hypothesized
a relationship between SES and academic dishonesty but have not tested their hypotheses
because they have not had access to accurate measures (Calabrese & Cochran, 1990; McCabe &
Trevino, 1997).
Several studies have called for more direct measures of student academic dishonest
behavior (Bolin, 2004; Callaway, 1998; Daniel, Blount, & Ferrell, 1991; Nathanson, Paulhus, &
Williams, 2006; Nowell & Laufer, 1997; Ward & Beck, 1990). Questionnaires may overestimate Academic 17 actual student cheating (Karlins, Michaels, & Podlogar, 1988; Nelson & Schaeffer, 1986), but
Finn (2001) suggested students may underreport cheating on questionnaires because it is a sensitive behavior.
Another problem with current measures of academic dishonesty is that different types of cheating have different prevalence rates (Newstead, Franklyn-Stokes, & Armstead, 1996; Nowell
& Laufer, 1997; Thorpe, Pittenger, & Reed, 1999). Newstead et al. conducted a factor analysis on 21 items defined as academic dishonesty. They found five factors that should be examined with regard to academic dishonesty rather than one overall cheating construct: plagiarism, collaborative cheating, collusion on exams, lying, and noncollaborative exam cheating.
Hall and Kuh (1998) indicated researchers are likely to observe different outcomes at different institutions because the institutional context in which cheating occurs is an important variable. But Brown and Emmett (2001) found it is not student behavior that is drastically different between groups, rather it is the way researchers assessed cheating. They found the number of cheating indicators was the strongest predictor of measured student cheating in 31 studies that took place over 33 years.
Who is Caught Cheating?
Because definitions of cheating and academic dishonesty vary, it is difficult to determine just how many students are actually caught cheating. Estimates seem to range from 3% (Wright
& Kelly, 1974; Singhal, 1982) to 7% (Bunn, Caudill, & Gropper, 1992) who are caught for their dishonest behavior, but these are the only three studies that ventured to estimate how many students are actually caught.
Institutional statistics about the number of academic dishonesty cases adjudicated have been reported for Virginia Polytechnic Institute and State University (Auer & Krupar, 2001), Academic 18
Rutgers University (Fishbein, 1993), Kansas State University (Marcoux, 2002), and the
University of Maryland (McCabe & Pavela, 1998). It is hard to speculate about who is caught
cheating because not a single study has described which students have been held accountable for
their behavior. The Center for Academic Integrity (2000) noted there are four main reasons for
differences in self-reported cheating rates and university statistics. The first is that there are cases
of dishonesty that go unnoticed. Second, students do not report each other. Third, instructors may prefer to ignore cheating. Fourth, instructors may decide to handle the matter informally.
Institutions with administrative systems in place that are supported by faculty will
probably see more reports of academic dishonesty from faculty members (Keith-Spiegel,
Tabachnik, Whitley, & Washburn, 1998; McCabe & Pavela, 1998). As faculty approve of
institutional procedure and become more aware of the process of addressing academic
dishonesty, they are more likely to report cases of identified academic dishonesty (Fishbein,
1993; Keith-Spiegel et al., 1998; Marcoux, 2002; McCabe, 2007).
Need for this Study
While many studies have been conducted to examine the pervasiveness of academic
dishonesty, the types of violations students engage in, the reasons students cheat, and
environments that encourage more honest behavior, there is no research examining what actually
happens to students after they cheat. We need information to decide if the current adjudication of
cases of academic dishonesty is working for our institutional goals and mission. Academic 19
CHAPTER III: METHODS
Research Problem
The purpose of this study was to determine the effects of an academic disciplinary process experience on future academic performance at Bowling Green State University (BGSU).
Using Astin’s (1991) I-E-O framework, the variables studied included inputs (gender, international student status, ACT score, high school GPA, age, and if a student tested into developmental courses), environments (intercollegiate athletic involvement, college, charge, and disciplinary outcomes), and outcomes (GPA following adjudication, retention to the university following adjudication, and disciplinary outcomes). Disciplinary outcomes were used as both an environmental variable and as an outcome variable depending on the specific research question.
Information was gathered about all BGSU students who were reported for academic dishonesty between the spring of 2004 and the summer of 2007. Longitudinal analysis allowed
examination of matriculation and retention for the entire data set and allowed more valid
comparisons of students who were reported for academic dishonesty and other students on
campus. Additionally, a profile of students who were caught cheating and who were reported to
the appropriate administrative channels is presented.
BGSU Academic Honesty Policy Rules and Guidelines
The Academic Charter for BGSU outlines definitions and policies for cases of academic
dishonesty. The Charter definitions are based largely on the work of Gehring, Nuss, and Pavela
(1986). The difference between the Gehring et al. definitions and the Academic Charter
definitions of academic dishonesty is in the student’s intent. Gehring et al. asserted student intent
is a necessary part of academic dishonesty, whereas the Academic Charter indicates intent is not
a necessary component for a behavior to be deemed academic dishonesty at BGSU. Specifically, Academic 20 the academic charter reads, “lack of intent shall not be a defense against a charge of violating this policy” (Academic Charter, Section C).
The policy defines cheating, forgery, bribery/threats, fabrication, plagiarism, and facilitation of academic dishonesty. The definitions in the policy are not supposed to be all inclusive; rather, they are designed to serve as a guide for appropriate behavior. All behavior is initially determined to be honest or dishonest by the instructor in a given course.
Once a case of alleged academic dishonesty has been identified, the instructor has
original jurisdiction. It is the instructor’s responsibility to have an initial conference with the student whose behavior is in question. The instructor needs to give the student an opportunity to explain him/herself and should give the student an opportunity to respond to any evidence the instructor has presented. After that initial conference the instructor has the responsibility to make a sanction decision about the case and to send a letter to the student’s academic dean outlining the decision and the nature of the infraction.
The dean’s office sends the student a letter notifying her/him of her/his right to an appeal of the decision. The dean’s office also works with the provost’s office to track cases of academic dishonesty so repeat offenders can be addressed by the academic dean. The academic dean retains jurisdiction over the case if the sanction the student is supposed to receive is suspension or worse, if the student is a repeat offender, or if the student is not currently enrolled in an instructor’s course when the academic dishonesty is identified.
There is an academic honesty committee that serves as the appellant group for students and instructors in all cases. A student who wishes to appeal may write to the coordinator of the academic honesty committee and the committee will review all documents associated with the case. The people on the committee will decide if they are going to hear the case based on the Academic 21 documents submitted. Once it decides it will hear a case, the committee can uphold, overturn, or overturn in part any decision. In an appeal “the appellant shall have the burden of proof and the
standard of proof is more likely than not” (Academic Charter, Section C). The academic honesty
committee is comprised of 18 faculty members, 6 undergraduate students, and 6 graduate
students (Academic Charter, Section E. i, ii, & iii). There is an administrative coordinator for the
academic honesty committee who arranges hearing times, conducts the hearings, and trains
members of the academic honesty committee. The coordinator may not vote on any part of any
case heard by the committee.
Possible sanctions for violations of the academic honesty policy include expulsion,
dismissal (suspension for at least a year), suspension (no longer than a year), suspended sanction,
withdrawn/fail (WF) from the course (transcripts are marked by the Registrar’s Office), and
partial or no credit on an assignment or exam. For offenses after graduation a degree can be
revoked (which means there is no possibility of reinstatement), a degree can be rescinded
(revoked with the possibility of reinstatement), students can receive a ‘WF’ for a course and/or
lose specific course credit (which may cause a degree to be rescinded until the course is made up).
Research Questions
The first question for this project was: What are the academic outcomes for students who have engaged in and who have been held accountable (through the formal disciplinary process) for academic dishonesty? The second research question for this project was: What is the demographic profile of cheating students who have been held accountable for their behavior?
The third research question was: How do outcomes for students who have gone through the Academic 22 formal disciplinary process compare to their peers? More detailed research questions fall under these overarching questions and include descriptive, group comparisons, and relationships.
Outcomes Following Academic Dishonesty
For each question I made post hoc decisions about demographic variables that would be controlled for in the analysis.
1. Do students with an academic dishonesty violation have a difference in subsequent GPA and
retention (to students with no violation)?
2. Controlling for post hoc selections (select input variables that were significant) use each
environmental variable as the independent variable (type of cheating as applicable,
intercollegiate athletic involvement, college, and disciplinary outcomes as applicable), to predict
dependent variable of interest (GPA and retention to the university).
3. For students who were reported for academic dishonesty:
3a. Were there different academic outcomes (retention and GPA after adjudication) between men
and women who were caught cheating?
3b. Were there different academic outcomes (retention and GPA after adjudication) for
academically underprepared students (low ACT, tested into developmental coursework) and
students who did not seem to be academically underprepared who were reported for academic
dishonesty (low and high ACT groups were defined using a median split)?
3c. Were there different academic outcomes (retention and GPA after adjudication) for students
who had lower pre cheating college GPAs than for students who had higher pre cheating college
GPAs after they were reported for academic dishonesty (low and high GPAs were defined using a median split)? Academic 23
3d. Were there different academic outcomes (retention and GPA after adjudication) depending on the age of the student who was reported for academic dishonesty?
3e. Were there different academic outcomes (retention and GPA after adjudication) for international compared to domestic students after adjudication of academic dishonesty?
3f. Were there different academic outcomes (retention and GPA after adjudication) for students who were involved in athletics versus those who were adjudicated for academic dishonesty who were not involved?
Descriptive Demographic Profile
The purpose of descriptive questions and analyses was to describe as completely as possible the population of students who were reported for academic dishonesty. For each of the constructs (demographics, cheating offense, offense outcome, and academic outcome) the numbers of cheaters and non-cheaters in each category were reported. The demographic variables were gender, college GPA before academic dishonesty, age, ACT score, testing into developmental course work, international student status, undergraduate/graduate student status, intercollegiate athletic membership, and college. Academic dishonesty offenses were cheating, fabrication, plagiarism, forgery, bribery, threats, substitution, stealing, misrepresentation, and facilitation. Possible outcomes following academic dishonesty were verbal warning, partial credit on assignment, no credit on assignment, failing grade in course, failing grade in course with
‘WF’ grade in course, and removal from the institution by way of suspension, dismissal, or expulsion. Academic outcomes measured were GPA and retention to the university.
4. Conduct group comparisons with regard to type of offense and penalty:
4a. Did men engage in more academic dishonesty than women? Academic 24
4b. Were high academic risk students (defined by low ACT scores and testing into developmental classes) more likely to engage in a certain type of academic dishonesty?
4c. Were students with lower college GPAs reported for more academic dishonesty than students with higher GPAs?
4d. Did older students have fewer violations of academic dishonesty than younger students?
4e. Were undergraduate students found to have more violations of academic dishonesty than graduate students?
4f. Were international students found to engage in more academic dishonesty than domestic students?
4g. Were students who were involved in intercollegiate athletics more likely to engage in academic dishonesty?
4h. Were students who were in certain academic majors more likely to engage in academic dishonesty?
Research Design
A quasi-experimental and descriptive research design was used for this study. Students cannot be randomly assigned to academic dishonesty violations or no academic dishonesty violations groups. It made the most sense to use a descriptive research design to describe student outcomes as they “naturally occur” (Hedrick, Bickman, & Rog, 1993).
Procedures and Data Collection
Confidentiality for participants was essential. All student identifying information was handled in the offices where it originated (the Office of Institutional Research and the Office of the Provost) and was funneled through the Office of Institutional Research where student names Academic 25 and identification codes were removed. Data about the entire population of students on campus in the four-year period was gathered. Thus, sampling was not an issue for this project.
The Use of Effect
Astin (1991) discussed the use of the Input-Environment-Outcome (I-E-O) model as a solution for “natural experiments” (p. 28). He advocated for natural experiments as a way to examine higher education as a more complex environment than a true experiment would allow.
The I-E-O model allows statistical controls for differences in populations that cannot be
randomly assigned. Astin asserted that we can make causal inferences from correlational data but
that we need to control for error. The I-E-O model is designed to control for error, this control
helps me to make more confident inferences about effects observed.
Limitations
For this study, the sample was drawn from a single institution. While it does examine the
population over a four-year time period caution needs to be exercised when attempting to
generalize results to other campus populations. An accompanying problem with generalizability
is the in vivo design; an experimental design would increase generalizability of results to other
campus populations.
This study assessed outcomes and described demographics associated with BGSU
students. The BGSU academic honesty process is specific to this campus. A further limitation of
a single institution study is that disciplinary outcomes cover only the range of possible penalties
for offenses of academic dishonesty at BGSU; there are other possible disciplinary outcomes at
other institutions.
Not all students who engage in academic dishonesty are caught and not all students who
are caught are referred to the formal academic disciplinary system in place on campus. Only a Academic 26 fraction of students who cheat are caught and referred. This has the potential to be a confounding variable in this study. If roughly half of the students on campus are cheating and only 3% are
being reported, there may be similarities between those who are lucky enough not to be reported
and the students investigated in this study.
There may be several reasons why student academic dishonesty goes unreported by
faculty members (Auer & Krupar, 2001). These reasons may include the fact that faculty prefer
to address the behavior in a case-by-case manner, and that the institutional process are
considered too bureaucratic. Assuming previous research concerning instructor tendencies to
report students for cheating to formal disciplinary process is low, only more severe cases may
have been reported centrally and were available to be examined as cases of academically
dishonest students.
Faculty definitions of cheating vary. Even faculty members in the same discipline have
been found to have differing opinions about what constitutes dishonesty (Brilliant & Gribben,
1993). Students who are reported for some violations might not have been reported if they had
engaged in the same behavior in another instructor’s class. Caution must be exercised when
interpreting comparisons between students who have been found to cheat and the rest of the
student population.
The academic outcomes examined in this research are not a direct function of
adjudication of cases of academic dishonesty. Academic outcomes are related to adjudication of
academic dishonesty because hearing penalties are often directly related to a student’s ability to continue courses. For example a failing grade in a course hurts a student’s GPA or may prevent him/her from enrolling in another course in which the first was a prerequisite. A further limitation related to the academic outcomes assessed is that if a student is removed from campus Academic 27 for academic dishonesty he/she obviously cannot be retained to the university and might eliminate possible results that would be observed if that student continued on campus.
There are several other academic outcomes that could have been assessed with regard to academic dishonesty, including, for example, credit hours earned, retaking the course, time to graduation, and continuance to graduate programs. Because of the nature of the available data, only select academic outcomes were investigated.
Finally, there is the issue of correlational results not equating to causal findings. While it is true that correlational analyses allow for only causal inferences, the use of several control variables does allow reduced error in results and makes it more likely that results are due to observed effects rather than measurement error. Academic 28
CHAPTER IV: RESULTS
The first research question for this study was: What are the academic outcomes for students who have engaged in and who have been held accountable for academic dishonesty?
The second research question was: What is the demographic profile of cheating students who have been held accountable for their behavior? And a third research question was: How do outcomes of GPA and retention for students who have gone through the formal disciplinary process compare to their peers?
In this chapter I describe the population of students who were reported for academic dishonesty and compare those students to their peers who have not been adjudicated for academic dishonesty. Next, I present a descriptive demographic profile of the population. Then group differences with regard to charges and penalties as well as retention and GPA following adjudication. Finally, I have included results from regression analyses.
Table 1 describes the population of students who were reported for academic dishonesty from the spring semester of 2004 through the summer semester of 2007. The university-wide comparison percentages are representative of the 2007 BGSU Fact Book, published by the Office of Institutional Research. Men, non-White students, international students, students in the low
ACT group, students who tested into developmental coursework, and student athletes were all overrepresented compared to the percentage of those populations in the rest of the university.
Students from the Arts and Sciences and Firelands College were slightly underrepresented and students from Business Administration, the College of Musical Arts, and Academic
Enhancement (students who are undecided about their majors and those who are conditionally admitted) represented more cases of academic dishonesty than would be expected given percentages of students from each of those colleges. Academic 29
Table 1 Descriptive Statistics: Comparison of Academic Dishonesty Population to University Population
n % All University %
Women 191 47.3 55.2 Gender Men 213 52.7 44.7
White 269 66.6 86.7 Race Non-White 135 33.4 13.3
Ohio resident 318 77.2 87.1
Residency Non-Ohio resident 48 11.7 9.7
International 38 9.2 3.1
Low (15-19) 127 42.3 28.4 ACT High (20-31) 173 57.7 71.6
Yes 163 75.5 40.3 Tested into Developmental Courses No 53 24.5 59.7
Yes 30 7.3 3.4 Student Athlete No 382 92.7 96.6
Arts & Science 81 20.0 24.9
Academic Enhancement 56 13.9 5.3
Business Administration 60 14.9 11.1
Education & Human Development 92 22.8 23.9
Firelands College 12 3.0 6.1 College Graduate College 35 8.7 11.5
Guest Students 9 2.2 0.0
Health & Human Services 24 5.9 8.4
Musical Arts 18 4.5 1.7
Technology 17 4.2 6.9
Academic 30
Other relevant information about the population studied was that students who engaged in academic dishonesty had an average high school GPA of 3.0 on a 4.0 scale and an age range of
18 to 52 with a mode age of 19 and a median age of 20.
To more completely describe students at Bowling Green State University, the following information was drawn from the 2007 BGSU Fact Book, published by the Office of Institutional
Research. There are 15,638 undergraduate and 2,981 graduate students (18,619 total) on campus.
Forty-two percent of all undergraduates live on campus. Six percent are 25 or older. All incoming students have a high school GPA of at least 2.0 on a 4.0 scale.
Do students with an academic dishonesty violation have a different subsequent GPA and retention rate (to students with no violation)? Students with an academic honesty violation had an average college GPA of 1.96 before adjudication of their violation and an average GPA of
2.30 following adjudication. It is important to note that for all GPA calculations, the sanction of an ‘F’ or a ‘WF’ in the course does factor into the GPA following adjudication. The average undergraduate GPA after fall 2006 for undergraduates was 2.73 in-state, 2.79 out-of-state, and
2.78 for international students (Office of Institutional Research). For graduate students the average GPA was 3.69 in-state, 3.64 out-of-state, and 3.53 for international students. So students who were reported for academic dishonesty had a lower average GPA than would otherwise be expected given the campus averages.
According to the Office of Institutional Research 79.7% of students enrolled at BGSU in
Fall 2006 were retained or graduated as of Fall 2007; 69.7% of the students who engaged in academic dishonesty were either retained or graduated from the University. Fewer students reported for academic dishonesty were retained to the University than otherwise would have been expected. Academic 31
Descriptive Demographic Profile of
Students Reported for Academic Dishonesty
Demographics explored included: gender, race, residency, ACT, testing into developmental courses, intercollegiate athletic membership, and college where the student
declared their major (see Table 1). Additionally, college GPA before adjudication (Table 2), and
age (Table 3) were examined for students who were reported for academic dishonesty. Both
undergraduate (n = 369; 91.34%) and graduate students (n = 35, 8.66%) were represented.
Table 2 GPA Ranges Before Adjudication
GPA n %
0.0-1.0 86 20.9
1.01-1.5 13 3.2
1.51-2.0 36 8.7
2.10-2.5 74 18.0
2.51-3.0 72 17.5
3.01-3.5 45 10.9
3.51-4.0 21 5.1
Missing 65 15.8 Academic 32
Table 3
Student Age at the Time of Academic Dishonesty: Comparison of Academic Dishonesty Population to University Population
Age n % All University %
17 and less 0 0.00 3.00
18-19 156 37.90 30.80
20-21 145 35.20 30.10
22-24 61 14.80 17.60
25-29 21 5.10 8.30
30-39 14 3.40 5.80
40-49 5 1.20 2.90
50 and over 2 0.50 1.50
An important aspect of this project was exploring the different charges/types of academic dishonesty students engaged in and also the penalties that were imposed after adjudication of misconduct. Cheating, collaboration, collusion, copying, fabrication, facilitation, falsification, forgery, misrepresentation, multiple (one instructor reported a student for several offenses that occurred simultaneously), plagiarism, and substitution were all defined by the instructor who reported a given student for academic dishonesty. It is possible that one instructor may have defined a behavior as copying and another would report the same behavior as cheating; this may explain why the use of the general term “cheating” was a popular offense to report. The Academic 33 following tables describe charges (Table 4) and penalties (Tables 5 and 6) that were observed in this population.
Table 4 Types of Charges Filed
Charge n %
Cheating 88 21.36
Collaboration 7 1.70
Collusion 2 0.49
Copying 2 0.49
Fabrication 7 1.70
Facilitating 10 2.43
Falsification 1 0.24
Forgery 11 2.67
Misrepresentation 1 0.24
Multiple 1 0.24
Plagiarism 231 56.07
Substitution 1 0.24
Missing Data 50 12.00
Academic 34
Table 5 Penalties Imposed Following Adjudication
Penalty n %
Warning 1 0.24
Resubmit assignment 4 0.97
Redo exam 1 0.24
Zero on assignment 183 44.42
Zero on exam 26 6.31
Partial credit on assignment 23 5.58
Partial credit on exam 9 2.18
C in course 4 0.97
D in course 1 0.24
F in course 20 4.85
I in course 2 0.49
NR in course 3 0.73
U in course 11 2.67
WF in course 71 17.23
Suspension 11 2.67
WF and Suspension 16 3.88
Dismissal 4 0.97
Expulsion 2 0.49
Drop final grade one letter 10 2.43
Charge removed 4 0.97
Missing Data 6 1.45
Academic 35
The observed values for several of the penalties were very low so some of the penalties were combined for further analysis. The penalties ‘C’ in the course and ‘D’ in the course were combined with dropping the final grade in the course by a letter grade to create the new variable, lower the final grade. Resubmit assignment and resubmit exam were combined to created resubmit assignment or exam. A grade of zero on an assignment and zero on an exam were combined to create the variable zero on an assignment or exam. The two partial credit penalties, assignment and exam, were combined to create the new variable partial credit on an assignment or exam. Suspension, dismissal, and expulsion were all combined to represent any student whose penalty reflected removal from the university environment for any period of time.
Table 6 Combined Penalties Following Adjudication
Charge n %
Warning 1 0.24
Resubmit assignment or exam 5 1.21
Zero on assignment or exam 209 50.73
Partial credit on assignment or exam 32 7.77
F in course 20 4.85
I, NR or U in course 16 3.88
WF in course 71 17.23
Suspension, Dismissal, or Expulsion 17 4.13
WF and Suspension 16 3.88
Lower final grade 15 3.64
Missing data 6 1.45
Academic 36
Finally, this project aimed to describe the academic outcomes of GPA and retention to the university following adjudication of academic dishonesty. Table 7 describes how many students were retained to BGSU following adjudication of academic dishonesty and Table 8 describes student GPA overall following adjudication. There are several missing values reported in Table 8 because students who were not retained could not earn a GPA following adjudication and students who graduated also could not earn a GPA following adjudication.
Table 7
Student Retention Following Adjudication
Retained n %
Yes 287 69.66
No 125 30.34
Table 8 GPA Following Adjudication
GPA n %
0.0-1.0 35 8.50
1.01-1.5 13 3.16
1.51-2.0 46 11.17
2.10-2.5 75 18.20
2.51-3.0 55 13.35
3.01-3.5 55 13.35
3.51-4.0 14 3.40
Missing 119 28.88
Academic 37
Group Differences: Retention, GPA, Charges, and Penalties
For this section Chi-Square analyses were used to determine if there were any significant differences for any input or environmental variable with respect to the specific charge of academic dishonesty and then also with respect to the type of penalty the student was assigned.
Regression equations were used to determine if there were relationships with regard to each input and environmental variable and student retention. Finally, analysis of variance was used to determine if there were differences in GPA following adjudication for each input and environmental variable.
Gender
There was no significant relationship between men and women and retention to the semester after adjudication; -2 Log Likelihood = 486.42, R2=.004, and was it statistically reliable in distinguishing retention; χ2(1)=1.61, p<.001. This model correctly classified less than 1% of
the cases. There was no significant difference between men and women with respect to GPA the semester following the adjudication of academic dishonesty; F(1, 293) = .89, p = .34. There were
no significant gender differences with regard to each charge; χ2 (11) = 11.69, p = .39 (see Table
9). There were no differences for gender and penalties; χ2 (10) = 16.05, p = .10 (see Table 10). Academic 38
Table 9 Charge by Gender
Gender Charge Women Men
Cheating n 41 45
% 11.58 12.71
Collaboration n 1 6
% 0.28 1.69
Collusion n 1 1
% 0.28 0.28
Copying n 0 2
% 0 0.56
Fabrication n 2 5
% 0.56 1.41
Facilitating n 5 5
% 1.41 1.41
Falsification n 0 1
% 0.00 0.28
Forgery n 4 7
% 1.13 1.98
Misrepresentation n 1 0
% 0.28 0.00
Multiple n 0 1
% 0.00 0.28
Plagiarism n 116 109
% 32.77 30.79
Substitution n 0 1
% 0.00 0.28
Academic 39
Table 10 Gender and Penalty Assigned
Gender Penalty Women Men
Warning n 0 1
% 0.00 0.25
Resubmit assignment or exam n 5 0
% 1.26 0.00
Zero on assignment or exam n 95 110
% 23.87 27.64
Partial credit on assignment or exam n 13 19
% 3.27 4.77
F in course n 10 10
% 2.51 2.51
I, NR or U in course n 7 9
% 1.76 2.26
WF in course n 34 34
% 8.54 8.54
Suspension, Dismissal, or Expulsion n 11 6
% 2.76 1.51
WF and Suspension n 4 12
% 1.01 3.02
Lower final grade n 7 8
% 1.76 2.01
Academic 40
Academic Preparedness
Students who were academically underprepared (defined by low ACT scores and testing into developmental coursework) were no more likely to engage in certain types of academic dishonesty or to receive certain penalties than students who were academically prepared.
ACT Scores
Students with low ACT scores (n = 127) were equally likely to be retained after
adjudication as students with high ACT scores (n = 173); -2 Log Likelihood = 360.98, R2=.001, and was not statistically reliable in distinguishing retention; χ2(1)=.311, p=.577. This model
correctly classified less than 1% of the cases. Students with low ACT scores did earn lower
GPAs following adjudication than students with high ACT scores; F(1, 116) = 4.42, p = .04, η2 =
.04. But after controlling for GPA before adjudication in an analysis of covariance this difference was no longer observed; F(1, 211) = 1.135, p = .275. There were no significant differences in low or high ACT groups and each charge; χ2 (9) = 13.585, p = .138 (see Table 11). There were
more students in the high ACT group who were reported for cheating (n = 44) compared to the
students in the low ACT group who were reported for cheating (n = 29). There were no
differences in low or high ACT groups and each penalty; χ2(10) = 10.934, p = .363 (see Table
12).
Academic 41
Table 11 ACT Group by Charge
ACT Group Charge Low (15-19) High (20-31)
Cheating n 29 44
% 10.82 16.42
Collaboration n 1 3
% 0.37 1.12
Copying n 0 2
% 0.00 0.75
Fabrication n 2 4
% 0.75 1.49
Facilitating n 0 5
% 0.00 1.87
Falsification n 0 1
% 0.00 0.37
Forgery n 7 2
% 2.61 0.75
Misrepresentation n 0 1
% 0.00 0.37
Plagiarism n 72 94
% 26.87 35.07
Substitution n 1 0
% 0.37 0.00
Academic 42
Table 12 Penalty Imposed and ACT Groups
ACT Group Penalty Low (15-19) High (20-31)
Warning n 0 1
% 0.00 0.34
Resubmit assignment or exam n 1 0
% 0.34 0.00
Zero on assignment or exam n 67 96
% 22.71 32.54
Partial credit on assignment or exam n 10 8
% 3.39 2.71
F in course n 5 7
% 1.69 2.37
I, NR or U in course n 5 8
% 1.69 2.71
WF in course n 20 33
% 6.78 11.19
Suspension, Dismissal, or Expulsion n 8 6
% 2.71 2.03
WF and Suspension n 6 5
% 2.03 1.69
Lower final grade n 1 6
% 0.34 2.03
Academic 43
Testing into Developmental Coursework
Students who tested into developmental coursework (n = 163) had statistically significant lower GPAs after adjudication; F(1, 92) = 6.45, p = .01, η2 = .06, than students who engaged in
academic dishonesty and did not test into developmental course work (n = 53). After controlling
for GPA before adjudication with an ANCOVA this difference was no longer observed; F(1,
153) = 1.26, p = .20. Students who tested into developmental coursework were less likely to be
retained following academic dishonesty than students who do not test into developmental work; -
2 Log Likelihood = 256.42, R2=.03, and was statistically reliable in distinguishing retention;
χ2(1)=6.11, p=.013. This model correctly classified 3% of the cases. There was no significant
difference in charges (see Table 13); χ2 (7) = 8.307, p = .306, or penalties (see Table 14); χ2 (9) =
11.405, p = .249, for students who tested into developmental coursework and those who did not.
Students who tested into developmental coursework were more likely to earn a zero on the assignment or exam, earn partial credit on an assignment or exam, earn an ‘F’ in the course, or earn both a ‘WF’ and suspension for their academic dishonesty than students who did not test into developmental coursework.
Academic 44
Table 13 Testing into Developmental Coursework and Charge
Tested into Developmental Coursework Charge Yes No
Cheating n 43 18
% 22.4 9.38
Collaboration n 3 1
% 1.56 0.52
Copying n 1 0
% 0.52 0.00
Fabrication n 2 1
% 1.04 0.52
Facilitating n 2 4
% 1.04 2.08
Falsification n 1 0
% 0.52 0.00
Forgery n 6 1
% 3.13 0.52
Plagiarism n 86 23
% 44.79 11.98
Academic 45
Table 14 Testing into Developmental Coursework and Penalty for Academic Dishonesty
Tested into Developmental Coursework Penalty Yes No
Resubmit assignment or exam n 2 0
% 0.93 0.00
Zero on assignment or exam n 91 26
% 42.52 12.15
Partial credit on assignment or exam n 17 4
% 7.94 1.87
F in course n 9 0
% 4.21 0.00
I, NR or U in course n 7 2
% 3.27 0.93
WF in course n 22 13
% 10.28 6.07
Suspension, Dismissal, or Expulsion n 5 4
% 2.34 1.87
WF and Suspension n 6 1
% 2.8 0.47
Lower final grade n 2 2
% 0.93 0.93
Academic 46
Student GPA before Adjudication
Students who had a lower GPA before adjudication (n = 63, µ = 1.70) had a statistically significant lower GPA after adjudication; F(1, 147) = 89.36, p < .001, η2 = .38, compared to
students with a higher GPA (n = 84, µ = 2.88). Students with a higher GPA before adjudication
were no more likely to be retained to the subsequent semester following adjudication than
students with a lower GPA; -2 Log Likelihood = 380.75, R2=.01, and was not statistically
reliable in distinguishing retention; χ2(1)=3.36, p=.067. This model correctly classified 1% of the
cases.
There were no statistically significant differences for students in higher (2.27 – 4.0) and
lower (0.0-2.26) GPA groups (defined using a median split) with regard to charges (see Table
15); χ2 (8) = 10.45, p = .24, or penalties (see Table 16); χ2 (10) = 17.47, p = .07. Students in the
high GPA group (n = 52) were reported for engaging in cheating more than students in the low
GPA group (n = 32). But students in the low GPA group were reported for many more cases of plagiarism (n = 102) than students in the high GPA group (n = 82). The students in the high GPA
group were more likely to earn a zero on the assignment of exam or partial credit on an
assignment or exam; where students in the low GPA group were more likely to receive an ‘I’,
‘NR’, ‘U’, or ‘WF’ in the course for their behavior.
Academic 47
Table 15 High or Low GPA Group and Charge
GPA Group Charge Low High
Cheating n 32 52
% 10.49 17.05
Collaboration n 2 5
% 0.66 1.64
Copying n 1 1
% 0.33 0.33
Fabrication n 4 3
% 1.31 0.98
Facilitating n 4 4
% 1.31 1.31
Falsification n 1 0
% 0.33 0.00
Forgery n 6 5
% 1.97 1.64
Multiple n 0 1
% 0.00 0.33
Plagiarism n 102 82
% 33.44 26.89
Academic 48
Table 16 High or Low GPA Group and Penalty
GPA Group Penalty Low High
Warning n 0 1
% 0.00 0.29
Resubmit assignment or exam n 3 2
% 0.88 0.59
Zero on assignment or exam n 79 101
% 23.17 29.62
Partial credit on assignment or exam n 11 18
% 3.23 5.28
F in course n 7 6
% 2.05 1.76
I, NR or U in course n 11 2
% 3.23 0.59
WF in course n 36 20
% 10.56 5.87
Suspension, Dismissal, or Expulsion n 7 5
% 2.05 1.47
WF and Suspension n 8 6
% 2.35 1.76
Lower final grade n 8 7
% 2.35 2.05
Academic 49
Age at Time of Academic Dishonesty
There were no statistically significant differences in retention between ages of students found engaging in academic dishonesty; -2 Log Likelihood = 487.92, R2<.001, and was not
statistically reliable in distinguishing retention; χ2(1)=.117, p=.732. But the oldest students had
significantly lower GPAs (μ = 0.94) following adjudication than all other students; F(6, 293) =
13.79, p < .001, η2 = .22. Table 17 represents each age group and their average GPA following
adjudication. There was no difference in type of academic dishonesty (see Table 18); χ2 (66) =
74.47, p = .22, or in types of penalties imposed (see Table 19); χ2 (60) = 66.03, p = .28 with regard to age.
Table 17
Descriptive Data About Each Age Group and GPA Following Adjudication
Age Mean GPA SD N
18 2.31 0.97 23
19 2.33 0.81 92
20 2.59 0.58 71
21 2.47 0.83 45
22 2.23 0.90 16
23 1.87 1.42 16
24-52 0.94 1.29 30
Academic 50
Table 18 Age of Student at Time of Adjudication and Charge
Age Charge 18 19 20 21 22 23 24-52
Cheating n 7 28 29 16 2 2 2
% 1.98 7.91 8.19 4.52 0.56 0.56 0.56
Collaboration n 0 1 2 1 1 1 1
% 0.00 0.28 0.56 0.28 0.28 0.28 0.28
Collusion n 0 0 0 0 0 1 1
% 0.00 0.00 0.00 0.00 0.00 0.28 0.28
Copying n 1 0 1 0 0 0 0
% 0.28 0.00 0.28 0.00 0.00 0.00 0.00
Fabrication n 0 3 2 1 0 1 0
% 0.00 0.85 0.56 0.28 0.00 0.28 0.00
Facilitating n 1 3 1 4 0 0 1
% 0.28 0.85 0.28 1.13 0.00 0.00 0.28
Falsification n 0 0 1 0 0 0 0
% 0.00 0.00 0.28 0.00 0.00 0.00 0.00
Forgery n 1 2 4 2 1 0 1
% 0.28 0.56 1.13 0.56 0.28 0.00 0.28
Misrepresentation n 0 1 0 0 0 0 0
% 0.00 0.28 0.00 0.00 0.00 0.00 0.00
Multiple n 0 0 0 1 0 0 0
% 0.00 0.00 0.00 0.28 0.00 0.00 0.00
Plagiarism n 19 75 40 22 14 15 40
% 5.37 21.19 11.3 6.21 3.95 4.24 11.3
Substitution n 0 1 0 0 0 0 0
% 0.00 0.28 0.00 0.00 0.00 0.00 0.00
Academic 51
Table 19 Penalty and Age of Student at Time of Adjudication
Age of student at time of Adjudication Penalty 18 19 20 21 22 23 24-52
Warning n 0 0 0 1 0 0 0
% 0.00 0.00 0.00 0.25 0.00 0.00 0.00
Resubmit assignment or exam n 1 0 1 0 0 1 2
% 0.25 0.00 0.25 0.00 0.00 0.25 0.50
Zero on assignment or exam n 16 69 44 25 13 8 30
% 4.02 17.34 11.06 6.28 3.27 2.01 7.54
Partial credit on assignment or exam n 2 6 11 5 2 2 4
% 0.50 1.51 2.76 1.26 0.50 0.50 1.01
F in course n 2 6 3 3 0 2 4
% 0.50 1.51 0.75 0.75 0.00 0.50 1.01
I, NR or U in course n 3 10 0 1 0 1 1
% 0.75 2.51 0.00 0.25 0.00 0.25 0.25
WF in course n 6 22 15 9 5 5 6
% 1.51 5.53 3.77 2.26 1.26 1.26 1.51
Suspension, Dismissal, or Expulsion n 0 4 1 6 2 1 3
% 0.00 1.01 0.25 1.51 0.50 0.25 0.75
WF and Suspension n 0 6 5 2 1 0 2
% 0.00 1.51 1.26 0.50 0.25 0.00 0.50
Lower final grade n 1 0 5 3 2 2 2
% 0.25 0.00 1.26 0.75 0.50 0.50 0.50
Academic 52
Student Status
Graduate students and undergraduate students were equally likely to be retained after adjudication; -2 Log Likelihood = 485.07, R2=.007, and was not statistically reliable in distinguishing retention; χ2(1)=2.965, p=.085. Graduate students earned statistically significant
lower GPAs following adjudication (μ= .34) than undergraduate students (μ= 2.41); F (1, 293) =
142.80, p < .001, η2 = .33.
There were differences between graduate and undergraduate students and the type of
cheating they engaged in; χ2 (11) = 37.764, p < .001 (see Table 20). Undergraduate students
engaged in more cheating, fabrication, and forgery than graduate students did. Both groups had
plagiarism as the charge that was most frequently reported. There were also differences in
penalties for graduate and undergraduate students (see Table 21); χ2 (10) = 40.319, p < .001.
Undergraduate students were more likely to earn the grade ‘WF’ in a course for academic
dishonesty than graduate students were.
Academic 53
Table 20 Charge by Graduate or Undergraduate Student Status
Student Status Charge Undergraduate Graduate
Cheating n 86 0
% 24.29 0.00
Collaboration n 5 2
% 1.41 0.56
Collusion n 0 2
% 0.00 0.56
Copying n 2 0
% 0.56 0.00
Fabrication n 6 1
% 1.69 0.28
Facilitating n 9 1
% 2.54 0.28
Falsification n 1 0
% 0.28 0.00
Forgery n 11 0
% 3.11 0.00
Misrepresentation n 1 0
% 0.28 0.00
Multiple n 1 0
% 0.28 0.00
Plagiarism n 203 22
% 57.34 6.21
Substitution n 1 0
% 0.28 0.00
Academic 54
Table 21 Penalty and Student Status
Student Status
Penalty Undergraduate Graduate
Warning n 1 0
% 0.25 0.00
Resubmit assignment or exam n 2 3
% 0.50 0.75
Zero on assignment or exam n 194 11
% 48.74 2.76
Partial credit on assignment or exam n 29 3
% 7.29 0.75
F in course n 15 5
% 3.77 1.26
I, NR or U in course n 14 2
% 3.52 0.50
WF in course n 65 3
% 16.33 0.75
Suspension, Dismissal, or Expulsion n 13 4
% 3.27 1.01
WF and Suspension n 16 0
% 4.02 0.00
Lower final grade n 11 4
% 2.76 1.01
Academic 55
International Students
International students earned statistically significant lower GPAs (n = 18, μ = 1.40) following adjudication than domestic students (n = 126, μ = 2.41); F(1, 277) = 42.91, p <.001, η2
= .14. International students still earned lower GPAs following adjudication when GPA before
adjudication was controlled for with an analysis of covariance; F(2, 262) = 12.45, p < .001, η2 =
.05. International students were also less likely to be retained following academic dishonesty
adjudication; -2 Log Likelihood = 472.22, R2=.02, and was statistically reliable in distinguishing
retention; χ2(1)=6.65, p=.01. This model correctly classified 2% of the cases.
There was a statistically significant difference in types of cheating with respect to
international and domestic students; χ2 (11) = 29.650, p =.002 (see Table 22). Domestic students engaged in more varied types of academic dishonesty than international students did. There was also a difference for international and domestic students with regard to penalties for academic misconduct; χ2 (9) = 29.614, p =.001 (see Table 23). International students were less likely to be
suspended following academic dishonesty.
Academic 56
Table 22 Charge and International Student Status
International Charge yes no
Cheating n 5 76
% 1.44 21.84
Collaboration n 2 5
% 0.57 1.44
Collusion n 2 0
% 0.57 0.00
Copying n 0 2
% 0.00 0.57
Fabrication n 0 7
% 0.00 2.01
Facilitating n 2 7
% 0.57 2.01
Falsification n 0 1
% 0.00 0.29
Forgery n 0 10
% 0.00 2.87
Misrepresentation n 0 1
% 0.00 0.29
Multiple n 0 1
% 0.00 0.29
Plagiarism n 19 207
% 5.46 59.48
Substitution n 0 1
% 0.00 0.29
Academic 57
Table 23 Penalty and International Student Status
International Penalty Yes No
Resubmit assignment or exam n 2 3
% 0.52 0.78
Zero on assignment or exam n 15 186
% 3.89 48.19
Partial credit on assignment or exam n 8 21
% 2.07 5.44
F in course n 4 16
% 1.04 4.15
I, NR or U in course n 2 14
% 0.52 3.63
WF in course n 2 65
% 0.52 16.84
Suspension, Dismissal, or Expulsion n 1 14
% 0.26 3.63
WF and Suspension n 0 14
% 0.00 3.63
Lower final grade n 4 11
% 1.04 2.85
Academic 58
Student Athletes
There were no statistically significant differences in retention; -2 Log Likelihood =
503.94, R2=.004, and was not statistically reliable in distinguishing retention; χ2(1)=1.76, p=.185,
or GPA; F (2, 278) = 2.11, p = .147, for student athletes and non-athletes in this population.
There were also no statistically significant differences in types of charges for athletes and non-
athletes; χ2 (11) = 14.068, p =.229 (see Table 24). There were statistically significant differences between athletes and non-athletes on the type of penalty received; χ2 (10) = 23.439, p =.009 (see
Table 25). Athletes were less likely to receive an ‘F’ in the course as a result of their academic misconduct. Academic 59
Table 24 Charge and Student Athlete Status
Student Athlete Charge yes no
Cheating n 13 75
% 3.59 20.72
Collaboration n 0 7
% 0.00 1.93
Collusion n 0 2
% 0.00 0.55
Copying n 0 2
% 0.00 0.55
Fabrication n 0 7
% 0.00 1.93
Facilitating n 1 9
% 0.28 2.49
Falsification n 0 1
% 0.00 0.28
Forgery n 1 10
% 0.28 2.76
Misrepresentation n 0 1
% 0.00 0.28
Multiple n 0 1
% 0.00 0.28
Plagiarism n 9 222
% 2.49 61.33
Substitution n 0 1
% 0.00 0.28 Academic 60
Table 25 Penalty and Student Athlete Status
Student Athlete Penalty Yes No
Warning n 1 0
% 0.25 0.00
Resubmit assignment or exam n 0 5
% 0.00 1.23
Zero on assignment or exam n 11 198
% 2.71 48.77
Partial credit on assignment or exam n 5 27
% 1.23 6.65
F in course n 0 20
% 0.00 4.93
I, NR or U in course n 0 16
% 0.00 3.94
WF in course n 8 63
% 1.97 15.52
Suspension, Dismissal, or Expulsion n 2 15
% 0.49 3.69
WF and Suspension n 1 15
% 0.25 3.69
Lower final grade n 1 14
% 0.25 3.45 Academic 61
Academic Colleges
Colleges examined for this study included: Arts & Science (A&S); Academic
Enhancement (ACE); Business Administration (BA); Education & Human Development (EAP);
Firelands College (FIR; a BGSU branch campus); Graduate College (GRA); Guest Students
(GST); Health & Human Services (HHS); Musical Arts (MUS); Technology (TEC). Students in
Academic Enhancement (i.e., those who are undecided about their majors and those who are conditionally admitted), Firelands College, Graduate College, and guest students earned significantly lower GPAs following adjudication than students in any other college; F (9, 293) =
37.742, p < .002, η2 = .546. There was no statistically significant difference in retention between
colleges following adjudication; -2 Log Likelihood = 455.13, R2=.08, and was statistically
reliable in distinguishing retention; χ2(1)=32.91, p<.001. This model correctly classified 8% of
the cases. There were statistically significant differences between colleges and types of charges
observed (see table 26); χ2 (99) = 204.661, p < .001. But there were no significant differences
with regard to colleges and penalties imposed; χ2 (90) = 111.448, p = .062 (see Table 27).
Academic 62
Table 26 Charge and College
College Charge A&S ACE BA EAP FIR GRA GST HHS MUS TEC
Cheating n 15 10 11 36 0 0 2 4 4 4
% 4.24 2.82 3.1110.17 0.00 0.00 0.56 1.13 1.13 1.13
Collaboration n 1 0 0 0 0 2 0 0 4 0
% 0.28 0.00 0.00 0.00 0.00 0.56 0.00 0.00 1.13 0.00
Collusion n 0 0 0 0 0 2 0 0 0 0
% 0.00 0.00 0.00 0.00 0.00 0.56 0.00 0.00 0.00 0.00
Copying n 0 0 0 0 0 0 0 0 2 0
% 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.56 0.00
Fabrication n 2 0 0 2 0 1 0 1 1 0
% 0.56 0.00 0.00 0.56 0.00 0.28 0.00 0.28 0.28 0.00
Facilitating n 1 0 5 0 0 1 2 1 0 0
% 0.28 0.00 1.41 0.00 0.00 0.28 0.56 0.28 0.00 0.00
Falsification n 1 0 0 0 0 0 0 0 0 0
% 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Forgery n 2 3 3 1 0 0 0 0 1 1
% 0.56 0.85 0.85 0.28 0.00 0.00 0.00 0.00 0.28 0.28
Misrepresentation n 0 0 1 0 0 0 0 0 0 0
% 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Multiple n 0 0 1 0 0 0 0 0 0 0
% 0.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Plagiarism n 50 38 22 44 12 22 5 16 6 10
% 14.12 10.73 6.21 12.43 3.39 6.21 1.41 4.52 1.69 2.82
Substitution n 1 0 0 0 0 0 0 0 0 0
% 0.28 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Academic 63
Table 27 Penalty and College
College Penalty A&S ACE BA EAP FIR GRA GST HHS MUS TEC
Warning n 0 0 1 0 0 0 0 0 0 0
% 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Resubmit assignment or exam n 0 0 1 1 0 3 0 0 0 0
% 0.00 0.00 0.25 0.25 0.00 0.75 0.00 0.00 0.00 0.00
Zero on assignment or exam n 45 27 24 45 10 11 7 11 14 11
% 11.31 6.78 6.03 11.31 2.51 2.76 1.76 2.76 3.52 2.76
Partial credit on assignment or exam n 6 3 6 9 0 3 0 2 2 1
% 1.51 0.75 1.51 2.26 0.00 0.75 0.00 0.5 0.5 0.25
F in course n 1 1 3 7 0 5 0 2 0 1
% 0.25 0.25 0.75 1.76 0.00 1.26 0.00 0.5 0.00 0.25
I, NR or U in course n 2 3 4 2 0 2 0 2 0 1
% 0.5 0.75 1.01 0.5 0.0 0.5 0.00 0.5 0 0.25
WF in course n 16 18 8 16 2 3 2 1 1 1
% 4.02 4.52 2.01 4.02 0.5 0.75 0.5 0.25 0.25 0.25
Suspension, Dismissal, or Expulsion n 3 1 4 4 0 4 0 1 0 0
% 0.75 0.25 1.01 1.01 0.00 1.01 0.00 0.25 0.00 0.00
WF and Suspension n 4 2 6 2 0 0 0 1 0 1
% 1.01 0.5 1.51 0.5 0.00 0.00 0.00 0.25 0.00 0.25
Lower final grade n 4 1 2 1 0 4 0 3 0 0
% 1.01 0.25 0.50 0.25 0.00 1.01 0.00 0.75 0.00 0.00
Academic 64
Regression Analyses
Logistic multiple regression was used to explain which independent variables (ACT score, testing into developmental coursework, GPA before adjudication, age, residency, academic honesty charge, college, and disciplinary penalties) were predictive of retention to the university. Linear, hierarchical multiple regression was used to explain which independent variables (Block 1: ACT score, testing into developmental coursework, GPA before adjudication, age, and residency) (Block 2: academic honesty charge, college, and disciplinary penalties) were predictive of the dependent variable of interest (GPA after adjudication). The independent variables were tested for possible multicollinearity. Tolerance and VIF collinearity values were within the acceptable range for all variables. GPA after adjudication was defined to include the next semester after adjudication in which the student enrolled and earned a GPA. This allowed the inclusion of several cases for students who did not enroll the semester following adjudication
(either by their own choice or because they were dismissed).
Regression results indicated an overall model fit of three predictors (testing into
developmental coursework, GPA before adjudication, and residency) that significantly predicted
student retention for those reported for academic dishonesty; -2 Log Likelihood = 156.94,
R2=.09, and was it statistically reliable in distinguishing retention; χ2(5)=14.01, p=.016. This
model correctly classified 9% of the cases. A summary of the regression model is presented in
Table 28. Wald statistics indicated that testing into developmental coursework, GPA before
adjudication, residency, and the charge collaboration significantly predicted retention. Academic 65
Table 28
Summary of Logistic Regression Analysis Predicting Student Retention Following Adjudication of Academic Dishonesty
Variable B SE Odds ratio Wald statistic
ACT -0.36 0.56 0.69 0.42
Test into developmental courses 2.02 0.69 7.56 8.48*
GPA before adjudication -0.84 0.31 0.43 7.47*
Age 0.07 0.29 1.08 0.06
Residency -3.61 1.49 0.03 5.85*
Resubmit assignment or exam -1.83 43168.83 0.16 0.00
0 on assignment or exam -21.21 15747.91 0.00 0.00
Partial credit on assignment or exam -21.81 15747.91 0.00 0.00
F in course -1.78 23487.74 0.17 0.00
‘I', 'NR', or 'U' in course -21.43 15747.91 0.00 0.00
‘WF' in course -21.32 15747.91 0.00 0.00
Suspension -23.87 15747.91 0.00 0.00
‘WF' and suspension -21.81 15747.91 0.00 0.00
A&S -1.26 1.38 0.28 0.83
ACE 0.13 1.32 1.14 0.01
BA -0.21 1.56 0.81 0.02
EAP -0.21 1.32 0.81 0.03
HHS -1.32 1.81 0.27 0.53
MUS -0.97 1.70 0.38 0.32
Cheating 0.54 0.51 1.72 1.12
Collaboration 4.19 2.03 66.09 4.27*
Copying -18.62 40192.97 0.00 0.00
Fabrication -19.76 22630.98 0.00 0.00
Falsification -2.01 1.94 0.13 1.07
Forgery -18.49 40192.97 0.00 0.00
Misrepresentation -1.35 2.25 0.26 0.36
*p<.05.
Academic 66
Regression results indicated an overall model of four items (ACT score, GPA before adjudication, graduate or undergraduate student status, and international student) that
2 2 significantly predicted student GPA after adjudication; R = .482, R adj = .469, F(10, 401) =
37.25, p < .001. This model accounted for 48.2% of the variance in GPA after adjudication. A summary of the regression model is presented in Table 29.
Table 29
Summary of Linear Regression Analysis Predicting Student GPA Following
Adjudication of Academic Dishonesty
Variable B SE B β
Step 1
ACT 0.17 0.08 0.09*
Test into developmental courses 0.10 0.11 0.04
GPA before adjudication 0.39 0.03 0.52*
Age -0.01 0.02 -0.02
Student status -0.48 0.16 -0.16*
Residency 0.14 0.10 0.11
International 0.67 0.22 0.23*
Step 2
College 0.02 0.01 0.06
Charge -0.01 0.01 -0.04
Penalty 0.00 0.01 0.02
2 2 Note. R = .48 for Step 1; ΔR = .01 for Step 2. *p<.05. Academic 67
Linear regression was used to determine if the independent variable, charge, was predictive of the dependent variable, penalty. Regression results indicated the charge does not
2 2 significantly predict penalty, R = .003, R adj = .001, F (1, 410) = 1.41, p = .236.
Finally, given the disproportionate percent of non-White students who were reported for academic dishonesty compared to the university population, I wanted to know if academic preparedness mediated the observed race effects. Specifically, after controlling for entering academic ability, were non-White students significantly more likely to be reported for academic dishonesty than White students? To find this, I needed to look at the whole BGSU student
population between spring 2004 and summer 2007. Institutional Research reported using the
variables, undergraduate/graduate status, race, ACT score, high school GPA, and GRE score for
all enrolled students a two-step logistic regression using academic dishonesty as the dependent variable was conducted. It examined the effect of race on being reported for academic dishonesty after controlling for pre-college ability (ACT or high school GPA, taken separately for undergraduates, and GRE score for graduate students). The results presented in Table 30 indicated being non-White made a student significantly more likely to be reported for academic dishonesty after both ACT and high school GPA were controlled for (for undergraduates, although the r squared values are small at .02 and .01, respectively), and that being a non-White student was not significantly related to being reported for academic dishonesty after GRE scores were controlled for (for graduate students). Academic 68
Table 30
Summary of Logistic Regression Analysis Predicting Academic Dishonesty Adjudication from Race
Variable B SE Odds ratio Wald statistic
ACT score -0.11 0.02 0.90 41.67*
High school GPA -0.45 0.10 0.64 21.46*
GRE Verbal 0.00 0.00 1.00 2.76
GRE Math 0.00 0.00 1.00 1.10
*p<.05.
Summary of Statistically Significant Findings
Students who tested into developmental coursework were less likely to be retained
following academic dishonesty than students who did not test into developmental work.
International students were also less likely to be retained following academic dishonesty
adjudication than domestic students were.
Students who had a lower GPA before adjudication had a significantly lower GPA after
adjudication compared to students with a higher GPA before adjudication. The oldest students in
the 24-52 age group had significantly lower GPAs following adjudication than all other students.
Graduate students earned significantly lower GPAs following adjudication than undergraduate
students. International students earned significantly lower GPAs following adjudication than
domestic students. Students in Academic Enhancement, Firelands College, Graduate College,
and guest students earned significantly lower GPAs following adjudication than students in other
colleges. Academic 69
There were differences in the types of academic dishonesty reported for several groups.
Undergraduate students were more likely to be reported for cheating, fabrication, and forgery than graduate students were. Domestic students were more likely to engage in several types of academic dishonesty where international students were only reported for cheating, collaboration, collusions, facilitating, and plagiarism. Students enrolled in different colleges were reported for different types of cheating depending on the college; for example, collusion was an offense only reported from the Graduate College and copying was only reported from the College of Music.
There were also differences in penalties reported for some groups. Undergraduate students were more likely to receive a ‘WF’; international students were less likely to be suspended; and athletes were less likely to be sanctioned to an ‘F’ in the course following academic dishonesty.
Testing into developmental coursework, GPA before adjudication, and residency predicted student retention for those reported for academic dishonesty. ACT score, GPA before adjudication, graduate or undergraduate student status, and international student status significantly predicted student GPA after adjudication. Also race, for undergraduates, predicted being reported for academic dishonesty. Academic 70
CHAPTER V: DISCUSSION
Summary of Significant Results
Students who tested into developmental coursework and international students were less likely to be retained following academic dishonesty. Students who had a lower GPA before adjudication, older students (24-52), graduate students, international students, students enrolled
in Academic Enhancement, Firelands College, or as guest students earned lower GPAs than
students in other comparable groups following adjudication of academic dishonesty. The type of
academic dishonesty students engaged in differed for graduate and undergraduates, college
where the student was enrolled, and for international students.
I found that testing into developmental coursework, a low GPA before adjudication, and
student residency predicted lower student retention. Also, ACT score, GPA before adjudication,
graduate or undergraduate student status, and international student status predicted lower GPA
following adjudication. Finally, race for undergraduates helped predict being reported for
academic dishonesty; non-White students were reported more frequently than would be expected
given the institutional population.
Findings
A consistent finding was that students who I labeled as academically underprepared were
more likely to be reported for academic dishonesty. Students with lower ACT scores, who tested
into developmental coursework, who were in Academic Enhancement, and had lower GPAs
were more likely to be reported for academic dishonesty. In fact, more students who were
reported for academic dishonesty were represented in the low ACT group than would have been
expected given the university population percentages. The percent of students represented in the
low ACT group was almost twice what should have been expected. Students who tested into Academic 71 developmental courses were overrepresented in the students who were reported for academic dishonesty. Three quarters of the students reported for academic dishonesty tested into developmental work where less than half of the students at the university tested into the same developmental work. Students who were caught for academic dishonesty and who tested into developmental coursework were less likely to be retained than students who did not test into developmental work. More students from Academic Enhancement (13.9%) engaged in academic dishonesty than would have been expected if there was an even distribution across campus, only
5.3% of BGSU students are Academic Enhancement students.
I also found that different penalties were assigned to students who were academically underprepared. Students who tested into developmental coursework were more likely to earn a zero on the assignment or exam, earn partial credit on an assignment or exam, earn an ‘F’ in the course, or earn both a ‘WF’ and suspension for their academic dishonesty than students who did not test into developmental coursework. Students who had a lower GPA before they were reported for academic dishonesty appeared to be more likely to receive an ‘I’, ‘NR’, ‘U’, or
‘WF’ in the course where students with a higher pre-adjudication GPA were more likely to earn a zero on the assignment of exam or partial credit on an assignment or exam.
Other findings that emerged from this study included: more non-White students were reported for academic dishonesty than would be expected given the percent of non-White students enrolled on campus. Thirty-three point four percent of the students reported for academic dishonesty were comprised of non-White students and only 13.3% of the university population was composed of non-White students.
While I did find more young students who were reported for academic dishonesty
(consistent with Cizek, 2003), the oldest students in this study had significantly lower GPAs Academic 72 following adjudication than all other students. Other findings included: undergraduate students
engaged in more cheating, fabrication, and forgery than graduate students. There were significant
differences between colleges and types of charges observed. There were more student athletes
reported for academic dishonesty than would have been expected given the university population
and athletes were found to be less likely to be assigned an ‘F’ in the course as a result of their
academic dishonesty. International students engaged in less varied types of academic dishonesty
than domestic students. And similar to the findings of Whitley (2001), my results suggest the
gender differences that once existed with regard to academic dishonesty may be eroding as I
found almost equivalent percentages of men and women reported for academic honesty
violations.
Discussion
Academic Preparedness
I found students who were academically underprepared for college (had lower grades,
lower ACT scores, and who tested into developmental coursework) were at a higher risk for
attrition and earned poorer grades following academic dishonesty adjudication. It is unclear if
academically underprepared students were engaging in more academic dishonesty, as suggested
by McCabe and Trevino (1997), or if they just represented more cases of reported academic
dishonesty than more prepared students. While I recognize that academic preparedness is not
equated to intelligence, it is interesting to note that it has been suggested students who have
lower intelligence would be more likely to cheat (Barnett & Dalton, 1981; Bushway & Nash,
1977; Daniel, Blount, & Ferrell, 1991; Drake, 1941; Leming, 1980). Diekhoff et al. (1996), Finn
(2001), and Graham et al. (1994) found cheaters had lower GPAs, cheating students were not
doing as well academically as non-cheating students, and that poorer performing students were in Academic 73 fact engaging in more academic dishonesty. Previous literature hypothesized that students who have lower ACT scores and lower high school grades are more likely to engage in academic dishonesty (Mustaine & Tewksbury, 2005) because they are not prepared to participate in college
level work. Bennett (2005) suggested students who cheat would be overrepresented by people
who have the minimum academic requirements to be admitted to university. Sheers and Dayton
(1987) hypothesized that there would be more cheaters in groups of students represented by a
lower academic ability. I believe my findings supported these hypotheses.
For students who were academically underprepared, it might also be that these were
students who were not appropriately prepared to produce the type of work they were being asked
for at the college level and they may have thought that academic dishonesty was their only way
to by successful. Because these students may not have been prepared for college level work, it is
possible they did not know the behavior they were engaging in was in fact academic dishonesty.
These students might represent more first generation college students and might not have the
knowledge about processes in college to be able to produce an appropriate effective response
when confronted by an instructor about potential academic dishonesty.
ACT Scores and GPA
The over-representation of students in the low ACT score group reported for academic
dishonesty may be evidence that students who were admitted to the university at the low end of
the acceptable ACT range were more likely to commit acts of academic dishonesty. Similar to
students who were in the low GPA group before adjudication; it is possible students in the low
GPA group committed more egregious offenses. It is also possible that students who are in the
low ACT group may be less likely to try to negotiate their sanctions with their professors. These
students may not have the nuanced ability to convince instructors that their intentions were not to Academic 74 commit academic dishonesty and as a result may face being reported centrally at greater numbers.
Developmental Coursework
It is possible that students who tested into developmental coursework were already at risk for leaving BGSU and being charged with academic dishonesty was enough to encourage them to leave the institution. It is also possible these students were more likely to engage in academic dishonesty so they were reported with more frequency. Students who tested into developmental coursework may have committed more egregious offenses so their sanctions seem more severe than other students who did not test into developmental work. It could also be possible that these students were not savvy enough to try to negotiate their sanctions with their instructors or even to have conversations with their instructors about options that might be available. They may earn more severe penalties including more likely to be reported centrally as a consequence of their behavior.
Types of academic dishonesty did not seem to make a difference to retention or to GPA after adjudication, but this could be because instructors differently define academically dishonest behaviors (Cizek, 1999; Clement, 2001); the inconsistencies might explain the lack of statistically significant results.
Student Race
Other literature has suggested that White and non-White students report they engage in academic dishonesty at similar rates (Cochran, Chamlin, Wood, & Sellers, 1999). So the difference observed in the number of non-White students reported this study is both unique and troubling and should be explored further in future research. Academic 75
I believe there are three possible explanations for this unique finding about student race.
The first is that it is possible non-White students are more likely to be first generation college students. They may not have the tacit knowledge of the university disciplinary system. Because of this subtle lack of knowledge they may not react appropriately when confronted by an instructor, leaving an instructor with no choice but to report their behavior as academic dishonesty. Students who understand the institutional process might be more effective at convincing an instructor that they “didn’t mean to cheat.” In this same vein, non-White students may not be aware that they can challenge the instructor and that they have rights in the disciplinary process. It is impossible to know how many students are confronted about academic dishonesty and then their cases are never reported centrally. This information would help me to make more sense of the finding about race helping to predict who is reported for academic dishonesty.
A second possible explanation is that because BGSU is a predominately White institution, students who are not White are easier to identify in a class, just as the only man in a class of 200 women might very well be the first name an instructor learns. The only non-White person, or one of few, in an equally large class might be more easily identifiable. When instructors know a student in a class they may pay more attention to that student’s behavior and that they have a heightened awareness of what that student is producing for the course.
Third, I believe that university disciplinary processes mirror society. While institutions disciplinary processes do not try to behave as legal processes, our processes do mirror the legal system. In the United States we see a disproportionate number of non-White people in the criminal justice system for a myriad of reasons. I believe that our campus systems mirror these same phenomena that have been observed in our criminal justice system. Academic 76
Age
Older students had lower GPAs following adjudication. This finding may be because older students may not know to drop classes or withdraw from school if they know they are not going to be successful. There also may be different motivations for academic dishonesty for traditional and non-traditional age students and this might be reflected in a students GPA following adjudication. It is probably unlikely that older students would describe cheating as a
“game” or “addiction” like Payne and Nantz (1994) found with more traditional age students.
Recognizing this, I would be interested in learning non-traditional students’ motivations for academic dishonesty.
Charges Reported
Undergraduates were reported for more cheating, fabrication, and forgery than graduate
students; this may be because the opportunities undergraduate and graduate students have to
engage in academic dishonesty are different. Undergraduates have larger classes and do not
know their instructors as well as graduate students. It is also possible that some individuals who
would have cheated are not admitted or are not interested in graduate school so they could not be included in this study. Both groups, undergraduates and graduate students, were reported most frequently for plagiarism. I speculate that students in both groups may not see plagiarism as academic dishonesty. Some students copy words, phrases, and sentences from the Internet without proper citation and do not seem to view this as dishonest behavior.
There were different charges reported in different colleges; this finding is probably due to
the fact that different colleges use different means to measure student performance. I would not
expect students to be reported for cheating on an exam if their college judges performance on
essays or laboratory work. Academic 77
Student Athletes
More athletes were reported for academic dishonesty than would have been expected; the involvement theories (Diekhoff, et al., 1996; McCabe, 2007; McCabe, & Trevino, 1996;
McCabe, & Trevino, 1997; Pino, & Smith, 2003; Whitley, 1998) that more involved students are more likely to engage in academic dishonesty seem to be true for this group. It may also represent more stress or pressure on this particular group of students to perform well (McCabe,
& Trevino, 1997). Student athletes were less likely to receive an ‘F’ in the course as a result of their academic misconduct. This may be because they have unique eligibility circumstances that are tied to their grades; instructors may understand this and respond accordingly with a sanction other than a failing grade. It is also possible that student athletes understand their unique eligibility requirements and if they are confronted with academic dishonesty they may be more proactive in trying to negotiate sanctions with instructors.
International Students
International students engaged in fewer types of academic dishonesty than domestic
students. This may be because definitions of academic dishonesty are culturally bound (Whitley
& Keith-Spiegel, 2001). Where forgery would be more clearly dishonest, some types of
plagiarism may fall into a gray area (international students were not reported for forgery but
several were reported for plagiarism). It could be that international students were reported for
academic dishonesty and they did not know their behavior was considered unacceptable in the
United States. International students were also less likely to be retained following adjudication
and they were less likely to be suspended than domestic students. I speculate that the lack of
retention with these students could be from a number of factors. They might be embarrassed
about their behavior so they return home. Another explanation could be that they were already Academic 78 time limited in their stay at BGSU and would not have been retained regardless of an academic dishonesty charge. With regard to being less likely to be suspended, it is possible that international students were not committing offenses of the same magnitude as domestic students so their sanctions were not as severe. It is also possible that the dean who would have been responsible for the suspension took their international status into consideration when determining a penalty and they may have given international students more lenient sanctions as a result.
Limitations Revisited
There were several limitations to this study. First, because this population was drawn from a single institution caution needs to be exercised when attempting to generalize these results to other campuses. The academic honesty process examined in this research is specific to
BGSU and disciplinary outcomes cover only the range of possible penalties for offenses of academic dishonesty at BGSU.
A second limitation of this research is that not all students who cheat are caught and not all students who are caught are referred to the formal academic disciplinary system. This may have been a confounding variable. There could be similarities between students who were reported and students who were not reported for academic dishonesty.
Third, faculty definitions of cheating vary. Students who were reported might not have been reported for the same behavior in another instructor’s class. Caution must be exercised when interpreting comparisons between students who have been found to cheat and the rest of the student population.
Suggestions for Future Research
Students who were academically underprepared were reported more often for academic dishonesty. This finding leads me to suggest future research should explore if these students are Academic 79 actually engaging in more academic dishonesty or if they are just more likely to be caught and reported for their behavior. It also leads me to question if there are different motivating factors for committing academic dishonesty for students who are academically prepared to complete college level work and students who may not have that same level of preparation.
Students who were reported for academic dishonesty had a different demographic profile than students in general. Future research should explore if we are recognizing academic dishonesty more in some student sub-populations, and also if we are more likely to report only egregious offenses. In this same vein I suggest future research include the variable of first generation college student to determine if there are subtle differences in negotiations that take place between students and instructors when instructors decide to address academic dishonesty in a case-by-case fashion.
From the much lower GPAs I observed with older students who were reported for academic dishonesty, future research should be conducted on nontraditional age college students who are engaging in academic dishonesty to determine if their motivating factors are similar to that of more traditional age students. I would hypothesize that there are different motivations for academic dishonesty and also different understanding about processes for nontraditional age college students.
Sanctions assigned to students following adjudication of academic dishonesty impact their retention to the university. Future research should explore sanctions imposed at other institutions to further explore if some sanctions are more effective for student retention. Future research should also explore if some sanctions are more effective in deterring recidivism. Academic 80
Implications and Recommendations
Findings from this study should encourage dialogue on campuses about types of sanctions being used and what the goals of sanctioning for academic dishonesty should be.
Discussions about the value of retaining a student and working to educate students about appropriate academic behavior are warranted. Developing alternative sanctions like an educational class about academic dishonesty (Stover & Kelly, 2005) might be appropriate for students who have been charged with academic dishonesty. Additionally, conversations about marking transcripts should also be considered (Mercer, 1990). Cloa (2002) suggested using the grade of an ‘XF’ to make punishments permanent on transcripts and to denote that the failing grade was due to academic dishonesty. The grade of ‘XF’ would also provide instructors with another option to address academic dishonesty in their classrooms.
I suggest encouraging consistency both in reporting and in definitions of academic dishonesty. Instructors need to make their expectations clear at the onset of every class (Auer &
Krupar 2001) and they need to understand their responsibility to report students they address even if they only give that student a warning.
There are several steps educators should take to address academic dishonesty. Many students have claimed they do not understand their behavior to be dishonest (Auer & Krupar,
2001). It has also been claimed that students plagiarize because they do not understand proper academic conduct (Nitterhouse, 2003; Roig, 1999). Roig (1999) noted students do not even understand how to properly paraphrase material. If this is true, working in several environments
(e.g., classroom, residence halls, athlete study tables) to expose students to an understanding of academic dishonesty may be one approach to prevent the behavior. Proactive prevention is the best way to address academic dishonesty on campus (McCabe & Pavela, 1998). Academic 81
Next, we need to help instructors understand academic dishonesty as defined by their campus policies, their campus system, and how to address behavior on their campus. Whitley and Keith-Spiegel (2002) reported faculty members largely have not been properly trained and do not know what institutional policies are with regard to academic honesty. Procedures vary from campus to campus and an orientation to campus procedures should be mandatory for all instructors. Genereux and McLeod (1995) reported instructors and their attention to cheating is a factor in students’ decisions to cheat or not cheat. “Faculty are the most critical persons on campus in preventing academic dishonesty” (Kibler, 1994, p. 101).
Clement (2001) indicated instructors differ in what they consider dishonest and when they believe a student needs to be taught about appropriate academic standards. Institutions should be doing outreach to all faculty members to try to encourage them to be clear and consistent when in comes to addressing student conduct. Policies are only as good as their enforcement and, while the responsibility for academic conduct ultimately lies with students, instructors are responsible for policy enforcement. The ways academic dishonesty are addressed need to be consistent (Gehring, Nuss, & Pavela, 1986; Jendrek, 1989; Kibler, 1994).
Policies that are in place need to be policies instructors can support and enforce (Hall &
Kuh, 1998). Hard, Conway, and Moran (2006) reported the more faculty know and understand the policy the more likely they were to try to prevent and challenge academic misconduct.
For every case of academic dishonesty that does occur in a classroom, even if the penalty is a warning, instructors need to report those students in a manner consistent with institutional policy. Reporting centrally is the only way we can track recidivism, encourage consistent enforcement and penalties, and provide students with their right to appeal an instructor’s decision. Academic 82
Conclusion
As Bowers (1964) suggested, academic dishonesty is clearly not in line with the values of higher education. Universities claim to teach values and support values in education (Pavela,
2007; Schwartz, 2000). I cannot imagine having a discussion about values in higher education with out including academic honesty in the conversation.
While there has been a paucity of outcomes research in all areas of student discipline, this study looked directly at outcomes related to adjudication of conduct using Astin’s (1993) I-E-O framework as a way to concretely assess the phenomenon of academic dishonesty. In response to a call for more direct measures of academic dishonesty (Barnett & Dalton, 1981; Bolin, 2004;
Callaway, 1998) this study was the first of its kind to actually describe students who have been found to have engaged in academic dishonesty. Findings from this study led me to suggest increasing faculty awareness of the academic dishonesty policy, more consistent outcomes for violators, and working to increase student understanding of academic honesty. Further, I
recommend future research continue to assess concrete outcomes for students with regard to
academic dishonesty.
Because academic honesty in scholarly work is clearly held in such high regard in
institutions of higher learning, it is obviously a phenomenon educators need to better understand.
This understanding will help us to more effectively teach our students the values we espouse in
higher education. Appreciating the outcomes associated with academic dishonesty can help us to
both practice our own values and teach students to practice those same values in the future.
Academic 83
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