Academic Dishonesty Among Students in Higher Education: A Cross-Sectional Study From Jordan

Abstract

Background: Since academic dishonesty compromises academic integrity (AI) and learning quality worldwide, it is essential to understand the frequency of this behavior among Jordanian students, as well as its contributing causes, to design effective preventive strategies. This study aimed to examine academic dishonesty from the perspective of Jordanian higher education students and to evaluate the influence of specific demographic factors on academic dishonesty. Methods: A cross-sectional study collected data from 301 undergraduate and postgraduate students in governmental and private universities across a range of specialties in Jordan. An online self-report questionnaire that included an engagement in academic dishonesty (EAD) survey was used for this purpose. A Kruskal–Wallis t-test was conducted to examine differences in students’ perceptions of academic dishonesty according to demographic characteristics. The Pearson r value was used to examine any correlations between age and academic dishonesty. Results: Among 301 participants, the average age score for university students’ EAD was moderately low, 2.27 (standard deviation = 0.76). The results also revealed statistically significant differences in students’ EAD scores in relation to the level of academic degree they were studying χ2 (2) = 6.99, p = 0.03, their academic major χ2 (2) = 6.93, p = 0.03, their desire to take up a particular specialty (t [61.01] = −2.53, p = 0.014), their knowledge of the university’s instructions on fraud and plagiarism (t [156.59] = −2.97, p = 0.003), having received previous punishment for fraud or plagiarism at university (t [13.66] = 4.97, p < 0.001), and their ages (r [300] = −0.146, p = 0.011). No significant differences were found between EAD scores with respect to academic year, university type, or current GPA. Conclusion: It is essential for universities to increase students’ awareness of what constitutes fraud and plagiarism and to cultivate a culture of AI. Prevention measures for academic dishonesty should also consider all the variables linked to dishonesty identified in this study. Further research is, however, necessary to thoroughly comprehend the fundamental causes of academic breakdown to propose culturally suitable solutions.