Testing a Model to Predict Online Cheating--Much Ado about Nothing

Autor/in	Beck, Victoria
Titel	Testing a Model to Predict Online Cheating--Much Ado about Nothing
Quelle	In: Active Learning in Higher Education, 15 (2014) 1, S.65-75 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1469-7874
DOI	10.1177/1469787413514646
Schlagwörter	College Students; College Faculty; Student Attitudes; Teacher Attitudes; Prediction; Predictor Variables; Grade Point Average; Higher Education; Cheating; Online Courses; Computer Assisted Testing; Least Squares Statistics; Wisconsin + Suchen Sie Ihr Suchwort? Collegestudent; Fakultät; Schülerverhalten; Lehrerverhalten; Vorhersage; Prädiktor; Hochschulbildung; Hochschulsystem; Hochschulwesen; Prellen; Online course; Online-Kurs
Abstract	Much has been written about student and faculty opinions on academic integrity in testing. Currently, concerns appear to focus more narrowly on online testing, generally based on anecdotal assumptions that online students are more likely to engage in academic dishonesty in testing than students in traditional on-campus courses. To address such assumptions, a statistical model to predict examination scores was recently used to predict academic dishonesty in testing. Using measures of human capital variables (for example, grade point average, class rank) to predict examination scores, the model provides for a comparison of R2 statistics. This model proposes that the more human capital variables explain variation in examination scores, the more likely the examination scores reflect students' abilities and the less likely academic dishonesty was involved in testing. The only study to employ this model did provide some support for the assertion that lack of test monitoring in online courses may result in a greater degree of academic dishonesty. In this study, however, a further test of the predictive model resulted in contradictory findings. The disparate findings between prior research and the current study may have been due to the use of additional control variables and techniques designed to limit academic dishonesty in online testing. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10