An Institutional Self-Study of Text-Matching Software in a Canadian Graduate-Level Engineering Program
Bibliographic record
Abstract
Abstract This institutional self-study investigated the use of text-matching software (TMS) to prevent plagiarism by students in a Canadian university that did not have an institutional license for TMS at the time of the study. Assignments from a graduate-level engineering course were analyzed using iThenticate®. During the initial phase of the study, similarity scores from the first student assignments (N = 132) were collected to determine a baseline level of textual similarity. Students were then offered an educational intervention workshop on academic integrity. Another set of similarity scores from consenting participants’ second assignments (n = 106) were then collected, and a statistically significant assignment effect (p < 0.05) was found between the similarity scores of the two assignments. The results of this study indicate that TMS, when used in conjunction with educational interventions about academic integrity, can be useful to students and educators to prevent and identify academic misconduct. This study adds to the growing body of empirical research about academic integrity in Canadian higher education and, in particular, in engineering fields.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".