Preservice secondary teachers’ beliefs about academic dishonesty: An attribution theory lens to causal search
Bibliographic record
Abstract
Academic dishonesty is an area of concern across all levels of education. While previous research has largely focused on what behaviours students engage in and what instructors do in response, little is known about why, and even less incorporates a theoretical framework. To contribute to the existing literature, our aim was to examine preservice secondary teachers’ beliefs about academic dishonesty. Moreover, we utilized Attribution Theory as our theoretical framework and examined how preservice teachers engage in causal search when presented with instances of academic dishonesty. Our results demonstrate that preservice teachers have strong beliefs about what is and what is not academic dishonesty; however, context matters. Indeed, when provided with descriptive scenarios compared to discrete behaviours, ratings of academic dishonesty were significantly higher in the former than the latter. Moreover, preservice teachers draw on multiple pieces of information when engaging in the causal search process, identifying not only facts but also embellishments not present in the scenario and highlighting their beliefs around academic dishonesty. Recommendations for educators and administrators for supporting students are provided, as well as limitations and directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".