Abstract:

In recent years, with the wide penetration of communication technologies, such as smartphones, email and social networks, side by side with the unlimited availability of information, academic dishonesty has gained momentum among university students (Stogner, Miller, & Marcum, 2013). In order to improve our understanding of academic dishonesty, this study is focused on the motivations for cheating and on the penalties for conducting such behavior.
This study is based on the analysis of 315 protocols of the Disciplinary Committee at The Open University of Israel. The protocols contain a description of the case and the penalties for all the academic dishonesty offenses committed by OUI students that were judged by the institutional Ethics Committee during the period 2012-2013. The study focused on the analysis of academic dishonesty behaviors, the reasons for conducting them, and the severity of penalties for violations of academic integrity. Data analysis was based on Pavela's (1997) framework of academic dishonesty types:
•Cheating - intentional use of prohibited study materials, information or any kind of aid, including consulting with other people;
•Plagiarism - use of content produced by others and presenting it without crediting the source, as if it were one's own;
•Fabrication - invention of information, data, or references that do not actually exist;
•Facilitation -intentionally helping others in violating the code of academic integrity.
Following previous studies that expanded Pavela's framework by addressing the impact of technology on academic dishonesty (Blau & Eshet-Alkalai, 2014, 2016; Blau, Eshet-Alkalai, & Rotem, 2014; Friedman, Blau & Eshet-Alkalai) in schools, this study distinguished between digital and "traditional" - analog dishonesty in the academia.
Murdock and Anderman's (2006) motivational model for committing academic dishonesty and the Self-Concept Maintenance Model (SCM; Mazar, Amir, & Ariely, 2008) were employed in the analysis of the motivations and reasons for students' dishonest behaviors. The motivational model explains academic dishonesty in terms of benefits and risks, while the SCM model predicts dishonest behaviors by the ability to maintain a positive view of self as an honest person, despite violating the ethical code.
Consistent with previous studies among pupils (Blau & Eshet-Alkalai, 2014, 2016), findings clearly indicate that the analog dishonesty was more common than the digital one. The most common academic dishonesty type was cheating, and none of the protocols presented a case of fabrication that was the most prevalent type of academic dishonesty reported by pupils (Blau & Eshet-Alkalai, 2014, 2016).
Regarding motivation for conducting offences, according to the students and consistent with SCM (Mazar et al., 2008), the most common reason for academic dishonesty behavior was the ability to maintain a positive view of self as an honest person, despite violating the ethical code. Interestingly, penalties for analog dishonesty were found to be more severe than those imposed for digital dishonesty. Surprisingly, female students were penalized more severely than male students, despite no significant gender differences in academic dishonesty types or in any other parameter explored in the study. Findings of this study shed light on the scope and roots of academic dishonesty and may assist institutions cope effectively and prevent this disturbing phenomenon.

Keywords:

Digital academic dishonesty, cheating, plagiarism, fabrication, facilitation, academic integrity in higher education, motivation for academic dishonesty.