Quizzing Students about their Writing: Implications for Deterring and Detecting Contract Cheating, and Promoting Academic Integrity and Greater Engagement
Bibliographic record
Abstract
Contract cheating is a significant concern in the higher education sector, and a multi-faceted approach focusing on student learning and growth, in addition to deterring and detection cheating, is necessary to address the issue. One way to support student learning is by encouraging active engagement in learning activities and assessments. To this end, we explored the utility Auth+ by Sikanai, an authorship verification platform that auto-generates six multiple-choice questions based on students’ writing submissions and generates scores based on responses to the questions. Auth+ is designed to encourage students to engage in the learning process and to facilitate the detection of potential contract cheating. Auth+ was implemented in a third-year computer science course, and 24 students shared their perceptions of its value for teaching and learning. Only 25% of students agreed that Auth+ would be useful in their studies but 62.5% agreed it would deter contract cheating. We also found an association between Auth+ scores and individual differences in working memory (τB = .391). Our findings suggest that, with further technology development, authorship verification platforms may be useful for promoting academic integrity and deeper engagement in learning at scale. Training educators to interpret the results and use them as part of a multi-faceted strategy for promoting academic integrity and reduce academic misconduct is important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".