A Conceptual Framework for Detecting Cheating in Online and Take‐Home Exams

Abstract

Selecting the right methodology to use for detecting cheating in online exams requires considerable time and effort due to a wide variety of scholarly publications on academic dishonesty in online education. This article offers a cheating detection framework that can serve as a guideline for conducting cheating studies. The necessary theories and related statistical models are arranged into three phases/sections within the framework to allow cheating studies to be completed in a sufficiently quick and precise manner. This cheating detection framework includes commonly used models in each phase and addresses the collection and analysis of the needed data. The model's level of complexity ascends progressively from a graphical representation of data and descriptive statistical models to more advanced inferential statistics, correlation analysis, regression analysis, and the optional comparison method and the Goldfeld-Quandt Test for heteroskedasticity. An instructor receiving positive results on the possibility of cheating in Phases 1 or 2 can avoid using more advanced models in Phase 3. Tests conducted on sample courses showed that models in Phases 1 and 2 of the proposed framework provided results effectively for over 70% of the test groups, saving users further time and effort. High-tech systems and low-cost recommendations that can mitigate cheating are discussed. This framework will be beneficial in guiding instructors who are converting from the traditionally proctored in-class exam to a take-home or online exam without authentication or proctoring. In addition, it can serve as a powerful deterrent that will alleviate the concerns that an institution's stakeholders might have about the reliability of their programs.

Collusion among Accounting Students: Data Visualization and Topic Modeling of Student Interviews

“…Our results also coincide fully with those of Duplaga and Astani (2010), D'Souza and Siegfeldt (2017), and Cronan et al. (2018) who report that perceptions of assignment difficulty and fairness possibly affect the intention to cheat.…”

Take-Home Exams in Higher Education: A Systematic Review

“…Freedman, 1968 [22] Personal reflections No Svoboda, 1971 [10] Personal comments based on experience No Marsh, 1980 [23] Hypothesis testing Yes Weber et al, 1983 [24] Hypothesis testing Yes Marsh, 1984 [25] Hypothesis testing Yes Grzelkowski, 1987 [26] Case study No Zoller and Ben-Chaim, 1989 [27] Survey + Case study Yes Murray, 1990 [28] Review No Fernald and Webster, 1991 [29] Case study No Haynie, 1991 [17] Hypothesis testing Yes Andrada and Linden, 1993 [8] Hypothesis testing Yes Ansell, 1996 [30] Collaborative THE (voluntary pairing) + final ICE No Norcini et al, 1996 [31] Hypothesis testing Yes Hall, 2001 [15] Pilot study with optional take-home test No Mallory, 2001 [2] Case study No Zoller, 2001 [12] Case study No Haynie, 2003 [32] Hypothesis testing Yes Bredon, 2003 [11] Multiple choice THE Yes Tsaparlis and Zoller, 2003 [33] Synthesizing from others Yes Giordano et al, 2005 [34] Hypothesis testing Yes Moore and Jensen, 2007 [35] Hypothesis testing Yes Williams and Wong, 2009 [1] Online survey, students reminded by e-mail No Frein, 2011 [36] Hypothesis testing Yes Giammarco, 2011 [6] Hypothesis testing Yes Lopez et al, 2011 [3] Case study No Rich, 2011 [4] Theoretical work No Marcus, 2012 [37] Review No Tao and Li, 2012 [38] Hypothesis testing Yes Hagström and Scheja, 2014 [39] Hypothesis testing Yes Rich et al, 2014 [40] Hypothesis testing Yes Sample et al, 2014 [41] Hypothesis testing Yes Johnson et al, 2015 [42] Case study Yes Downes, 2017 [43] Review No D'Souza and Siegfeldt, 2017 [44] Hypothesis testing Yes Lancaster and Clarke, 2017 [45] Review No…”

Interaction of Proctoring and Student Major on Online Test Performance

“…Strategies that emerged from studies by Beck (2014), D'Souza and Siegfeldt 2017 withholding answers until the exam is completed by all students; avoiding high-stakes tests; and developing a supportive and trusting community within the class (Beck, 2014;D'Souza & Siegfeldt, 2017;McCabe et al, 2001;Rogers, 2006;WCET, 2009). These techniques have been assessed in combination rather than separately in an experimental format, so it is not yet possible to know which approaches have been more effective (e.g., Beck, 2014;Cluskey, Ehlen & Raiborn, 2011;McGee, 2013), Some researchers hold that appropriate instructional design of open courses can eliminate cheating, particularly when assessment relies upon application of concepts rather than memorization of facts (Cluskey et al, 2011;McGee, 2013).…”

“…Previous research has used the finding of significant differences in scores between proctored and unproctored tests as a measure of cheating (Beck, 2014;D'Souza & Siegfeldt, 2017). An elegant study by Fask et al (2014) statistically controlled for the effects of online versus face-to-face examination processes, and found that students were more likely to cheat in unproctored online tests.…”

“…Research on student perceptions about integrity indicates that student dishonesty is a significant concern, especially in online classrooms (Berkey & Halfond, 2015;D'Souza & Siegfeldt, 2017;Kitahara, Westfall, & Mankelwicz, 2011), and that steps taken to ensure a fair environment when it comes to assessment are supported by students as well as instructors. Faculty who teach online are encouraged to use various pedagogical strategies to develop a relationship of trust with their students (WCET, UT Telecampus, & Instructional Technology Council (WCET), 2009).…”