Collusion among Accounting Students: Data Visualization and Topic Modeling of Student Interviews

摘要

Although classroom cheating violates academic standards of behavior, it occurs frequently.Although the research on cheating is extensive, few researchers have interviewedstudents directly involved in cheating behaviors.We explore interview responsesgathered from a cohort of graduate accounting students, some of whom colluded on anassignment, whereas others did not.We use Latent Dirichlet Allocation (LDA), a powerfultext mining algorithm, as our primary tool to explore the underlying topical structureof the interviews and to demarcate subtle differences among students’ reactions to andexplanations of their experience. Because LDA does not impose or require a priori theories,we use it to provide ideas for future research rather than to test extant theories aboutclassroom collusion. We identify five primary topics that emerged from the accountingstudents’ reflections: (1) general course context (including honor code), (2) the rigor ofthe assignment, (3) student teams as support mechanisms, (4) the perceived repercussionsof cheating (colluding), and (5) personality differences between the tax and audittrack students. We find subtle language differences between colluders and noncolluders.Colluders considered the nature of the assignment and the difference between taxand audit majors more significant than noncolluders did. Additionally, the role of teamsand the general institutional context were somewhat less relevant for colluders than for noncolluders. We conclude by exploring ethical and pedagogical implications of structuringcourses as heavily team based for teaching and future research purposes.