Testing a model to predict online cheating-Much ado about nothing
Testing a model to predict online cheating-Much ado about nothing
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DOI：
10.1177/1469787413514646
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发表时间：
2014-03-01
影响因子：
5
通讯作者：
Beck, Victoria
中科院分区：
文献类型：
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作者：
Beck, Victoria
关于学生和教职员工对考试学术诚信的看法，已经写了很多文章。目前，人们的担忧似乎更多地集中在在线考试上，通常是基于坊间的假设，即在线学生比传统校园课程的学生更有可能在考试中进行学术欺诈。为了解决这些假设，一种预测考试成绩的统计模型最近被用来预测考试中的学术欺诈。该模型使用人力资本变量的衡量标准(例如，平均绩点、班级排名)来预测考试成绩，提供了R-2统计数据的比较。该模型认为，人力资本变量越多地解释考试成绩的差异，考试成绩就越有可能反映学生的能力，考试中涉及学术不诚实的可能性就越小。使用该模型的唯一一项研究确实为以下断言提供了一些支持，即在线课程中缺乏考试监督可能会导致更大程度的学术欺诈。然而，在这项研究中，对预测模型的进一步测试得出了相互矛盾的结果。之前的研究和目前的研究之间的不同发现可能是由于使用了额外的控制变量和技术，旨在限制在线测试中的学术不诚实。
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 R-2 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.