Matching Items (2)
Description
Perceptions of legitimacy are an important antecedent of rule-abiding behavior. However, most research on the link between legitimacy and compliance has focused on legal authorities (i.e., police, courts, and corrections). To help fill this gap, the present study investigates the relationship between students' perceptions of the legitimacy of institutional authority and compliance with a code of conduct in a university context. This study uses cross-sectional data from pencil-and-paper surveys administered to 517 individuals 18 years and older that were enrolled in 12 undergraduate classes at a large southwestern university. Results from the multivariate regression models show that procedural justice judgments are associated with perceived legitimacy. The evidence also supports the link between legitimacy and compliance in that the former is inversely related to students' behavioral intentions to cheat on an exam. However, legitimacy was not significantly associated with plagiarism. Overall, findings support the application of the process-based model of regulation to the university context in regards to academic misconduct. In addition to contributing to the process-based model literature, this study emphasizes the utility of the process-based model as a guide for the development of fair processes, in order to reduce the prevalence of student academic misconduct.
ContributorsBain, Stacy Nicole (Author) / Reisig, Michael D. (Thesis advisor) / Holtfreter, Kristy (Committee member) / Ready, Justin (Committee member) / Arizona State University (Publisher)
Created2015
Description
This study investigated the ability to relate a test taker’s non-verbal cues during online assessments to probable cheating incidents. Specifically, this study focused on the role of time delay, head pose and affective state for detection of cheating incidences in a lab-based online testing session. The analysis of a test taker’s non-verbal cues indicated that time delay, the variation of a student’s head pose relative to the computer screen and confusion had significantly statistical relation to cheating behaviors. Additionally, time delay, head pose relative to the computer screen, confusion, and the interaction term of confusion and time delay were predictors in a support vector machine of cheating prediction with an average accuracy of 70.7%. The current algorithm could automatically flag suspicious student behavior for proctors in large scale online courses during remotely administered exams.
ContributorsChuang, Chia-Yuan (Author) / Femiani, John C. (Thesis advisor) / Craig, Scotty D. (Thesis advisor) / Bekki, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015