Evaluating the Psychometric Properties of a Questionnaire on Reasons Influencing Student to Plagiarize and Comparing the Perception of Teachers and Students
Bibliographic record
Abstract
Translator Plagiarism in undergraduate programs has been an increasing concern for teachers and administrators, since its propagation in recent years, due to the COVID-19 pandemic and rapid evolution of generative artificial intelligence. It is by better understanding the reasons inciting students to plagiarize that actors in universities can implement the required support mechanisms to prevent plagiarism and promote academic integrity. As part of an international partnership on plagiarism prevention, we developed questionnaires administered to 1357 teachers and 4661 students across 31 universities in North America and Europe. The respondents identified their perceptions of reasons why students plagiarize by selecting among a list of 31 items. Then, we conducted exploratory and confirmatory factorial analyses to evaluate the psychometric properties of the questionnaire, and we propose a model allowing for the comparison of perceptions between teachers and students. The findings allowed for the validation of the factor structure in three theoretical dimensions: task characteristics, subjective norms, and personal characteristics. When comparing results in both groups, teachers are significantly more likely than students to perceive subjective norms as an incentive to plagiarize, whereas for students it is the components of the task that prevails. Finally, we suggest further scientific exploration of contextual or individual factors influencing the empirical structuring of responses, such as the impact of cultural or motivational variables. Translator Translator Translator
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".