Pre-Service Teachers Beliefs about Plagiarism: An Attribution Theory Lens
Bibliographic record
Abstract
There is a growing body of research examining why students engage in plagiarism, and what they know about plagiarism, but little of this research is conducted from a theoretical perspective. Moreover, the perspectives of preservice teachers are important to investigate as they have been described as the “future gatekeepers of academic integrity” (Fontaine et al., 2020). Therefore, the purpose of this study was twofold. First, to examine the opinions of preservice teachers in terms of what constitutes plagiarism. Second, following the principles of Attribution Theory, to investigate how the controllability (e.g., intentional, or accidental plagiarism) of the act of plagiarism impacted participants' beliefs concerning (a) responsibility, (b) emotions, (c) help-giving, and (d) reporting. We used a within-person repeated measures design with three levels of controllability manipulated through hypothetical scenarios of plagiarism to collect data from 201 pre-service teachers. Overall, preservice teachers had strong opinions about plagiarism (e.g., It is always wrong to plagiarize). Moreover, when scenarios included students who engaged in plagiarism that was controllable, participants were more likely to perceive the student as responsible, felt anger towards them, support punishment, and recommend reporting the student, than when the act of plagiarism was not seen as controllable. We provide recommendations for instructors and administrators for supporting students. Moreover, the results and recommendations here will be used to foster discussion about the controllability of cheating and the associated cognitions, emotions, and actions. This conversation will address fostering a culture of academic integrity from a theoretical perspective to support faculty and students.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | medium |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.036 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".