Engineering Integrity: Using text-matching software in a graduate level engineering course
Bibliographic record
Abstract
Academic misconduct is an unfortunate reality for many post-secondary level educators across disciplines; however, there is currently a paucity of Canadian research on Academic Integrity (Eaton, 2018). This study describes an inter-disciplinary project to investigate the potential for text-matching software to prevent and avoid plagiarism by graduate level engineering students. Conceptual/Theoretical Framework: Our study was informed by the potential for text-matching software to help students understand and avoid plagiarism (Zaza & McKenzie, 2018) and faculty identify instances of plagiarism in an engineering course (Cooper & Bullard, 2014). Although text-matching software has been commercially available since the 1990s, its acceptance within academic contexts is uneven. Reasons for this are manifold, but the most commonly expressed concerns are about a) the punitive nature of the software use; b) the potential for it to be used as a tool for cheating students to “beat the system”, and c) privacy concerns (Savage, 2004). Methodology / Approach: In this project, approved by the institutional REB, assignments submitted in a graduate-level engineering communication course were analyzed using text-matching software, Ithenticate. The first phase of the study involved collecting baseline data from students enrolled in a graduate-level Engineering course (N=132). As per REB protocol, individual results were not shared with the professor or teaching assistants and sharing of aggregated results is not permitted until after February 15, 2019. In our presentation, we share baseline results, as well as outcomes of the second phase of the research, in which the research associate revealed the deception, explained the study, and solicited consent from students to have their next assignment harvested and analyzed. The research associate also introduced the software and provided a workshop on academic integrity including strategies for avoiding plagiarism, such as paraphrasing. Subsequent to these workshops, assignments written by consenting participants were analyzed with Ithenticate to determine whether a reduction in textual similarity occurred. Results / Findings: The results of this study indicate that text-matching software can be useful to students and educators to prevent and identify academic misconduct. This study will add to the growing body of empirical research about academic integrity in Canada and in particular, in engineering contexts.
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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 | 0.058 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".