Online education continues to grow, bringing opportunities and challenges for students and instructors. One challenge is the perception that academic integrity associated with online tests is compromised due to undetected cheating that yields artificially higher grades. To address these concerns, proctoring software has been developed to address and prevent academic dishonesty. The purpose of this study was to compare online test results from proctored versus unproctored online tests. Test performance of 147 students enrolled in multiple sections of an online course were compared using linear mixed effects models with nearly half the students having no proctoring and the remainder required to use online proctoring software. Students scored, on average, 17 points lower [95% CI: 14, 20] and used significantly less time in online tests that used proctoring software versus unproctored tests. Significant grade disparity and different time usage occurred on different exams, both across and within sections of the same course where some students used test proctoring software and others did not. Implications and suggestions for incorporating strategic interventions to address integrity, addressing disparate test scores, and validating student knowledge in online classes are discussed.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We measure the other-regarding behavior in samples from three related populations in the upper Midwest of the United States: college students, non-student adults from the community surrounding the college, and adult trainee truckers in a residential training program. The use of typical experimental economics recruitment procedures made the first two groups substantially self-selected. Because the context reduced the opportunity cost of participating dramatically, 91% of the adult trainees solicited participated, leaving little scope for self-selection in this sample. We find no differences in the elicited other-regarding preferences between the selfselected adults and the adult trainees, suggesting that selection is unlikely to bias inferences about the prevalence of other-regarding preferences among non-student adult subjects. Our data also reject the more specific hypothesis that approval-seeking subjects are the ones most likely to select into experiments. Finally, we observe a large difference between self-selected college students and self-selected adults: the students appear considerably less pro-social. Terms of use: Documents in

Traditional and online university courses share expectations for quality content and rigor. Student and faculty concerns about compromised academic integrity and actual instances of academic dishonesty in assessments, especially with online testing, are increasingly troublesome. Recent research suggests that in the absence of proctoring, the time taken to complete an exam increases significantly and online test results are inflated. This study uses a randomized design in seven sections of an online course to examine test scores from 97 students and time taken to complete online tests with and without proctoring software, controlling for exam difficulty, course design, instructor effects, and student majors. Results from fixed effects estimated from a fitted statistical model showed a significant advantage in quiz performance (7-9 points on a 100 point quiz) when students were not proctored, with all other variables statistically accounted for. Larger grade disparities and longer testing times were observed on the most difficult quizzes, and with factors that reflected the perception of high stakes of the quiz grades. Overall, use of proctoring software resulted in lower quiz scores, shorter quiz taking times, and less variation in quiz performance across exams, implying greater compliance with academic integrity compared with when quizzes were taken without proctoring software.

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Assessing the predictive accuracy of black box classifiers is challenging in the absence of labeled test datasets. In these scenarios we may need to rely on a human oracle to evaluate individual predictions; presenting the challenge to create query algorithms to guide the search for points that provide the most information about the classifier's predictive characteristics. Previous works have focused on developing utility models and query algorithms for discovering unknown unknowns -misclassifications with a predictive confidence above some arbitrary threshold. However, if misclassifications occur at the rate reflected by the confidence values, then these search methods reveal nothing more than a proper assessment of predictive certainty. We are unable to properly mitigate the risks associated with model deficiency when the model's confidence in prediction exceeds the actual model accuracy. We propose a facility locations utility model and corresponding greedy query algorithm that instead searches for overconfident unknown unknowns. Through robust empirical experiments we demonstrate that the greedy query algorithm with the facility locations utility model consistently results in oracle queries with superior performance in discovering overconfident unknown unknowns than previous methods.