Teaching the Teachers: To What Extent Do Pre-service Teachers Cheat on Exams and Plagiarise in Their Written Work?
Bibliographic record
Abstract
Abstract Very little is known about preservice teachers’ actions when it comes to plagiarizing and cheating in their university work. This is particularly the case in Quebec, Canada. It is important to know to what extent these students commit academic misconduct as they will ultimately become the role models who will shape future generations of learners. This chapter reports on a study of this important issue. An online questionnaire was used to survey preservice teachers (n = 573) in five Quebec universities in winter 2018. The majority of participants were between the ages of 18 to 25 and were studying to be kindergarten, primary, special education or high school teachers. The questionnaire contained items about demographic information as well as items on methods of cheating, peers’ influence, perception of control, goal of performance and engaging in studying. Preservice teachers also answered questions that were used to control for social desirability bias. Results showed that some of them reported participating in academic misconduct. Fewer participants reported cheating on exams while studying at university (15.2%) than when they were in high school (34.9%). They believe that the best ways to plagiarise on written assignment are reusing one’s previous work (47.6%), asking somebody else to do the assignment (38.6%), and collaborating with peers (37.2%) while the best ways to cheat on exams would be using hidden material (63%), looking at the neighbour’s copy (55.7%) and using electronic devices (31.9%). Four interpretations for the preservice teacher actions are given: they commit academic misconduct because they want to succeed, because they have poor studying habits which lead them to make poor decisions, because of the cheating culture in which they evolve, and because of the cheating patterns they develop. Recommendations for teacher education programs conclude the article.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".