000			02161nam a22002417a 4500
003			ZW-GwMSU
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008			241111b |||||||| |||| 00| 0 eng d
022			_a1469-7874
040			_aMSU _bEnglish _cMSU _erda
050	0	0	_aLB2300 ACT
100			_aVictoria Beck _eauthor
245			_aTesting a model to predict online cheating—Much ado about nothing _ccreated by Victoria Beck
264			_aThousand Oaks: _bSage Publications, _c2014.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_aActive Learning in Higher Education _vVolume 15, number 1 ,
520	3		_aMuch 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.
650			_aOnline cheating _vPredicting academic dishonesty
856			_udoi.org/10.1177/1469787413514646
942			_2lcc _cJA
999			_c168198 _d168198