Evaluation of a Tutorial Designed to Promote Academic Integrity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Academic integrity violations undermine principles of integrity and the quality of education. Reducing the prevalence of dishonesty in scholarly work requires a multi-faceted approach (Stephens, 2016), which may include the implementation of e-learning tutorials. Tutorials and other brief educational interventions increase students’ perceived knowledge and understanding of academic integrity and related topics (Stoesz & Yudintseva, 2018); however, it is unclear from the literature which students benefit most from completing them. In two studies, secondary (i.e., middle and high) school students were recruited to complete an e-learning tutorial and surveys about academic integrity, approaches to learning, motivation for learning, and personality. 88 students participated in an online study, but only 15 participants completed the tutorial. Knowledge and perceived seriousness of academic integrity violations increased significantly in this small sample; these changes were not evident in the remaining participants. A follow-up study with 90 students tested in face-to-face classroom sessions confirmed the results of the first study. Moreover, the changes in perception were larger for the youngest and oldest participants compared to the middle age group, and were correlated with use of deep learning strategies and agreeableness. Overall, the findings provide evidence for the effectiveness of academic integrity tutorials, and suggest individual difference factors must be considered when designing and implementing brief educational interventions. Examining behaviour change and long-term outcomes for secondary school students, and exploring the influences of learning environment and teacher characteristics on learning the values of academic integrity are important avenues for future research.
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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 | 0.021 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it