What Are ESL Students' Academic Integrity Challenges and How Can Universities Help?
Bibliographic record
Abstract
Plagiarism is becoming more widespread at Canadian universities, but what are English-as-asecond-language (ESL) student perspectives on their challenges to avoid plagiarism and university strategies to support students?This paper presents a study of English-for-academic-purpose (EAP) writing students at a Canadian university.The study employed semi-structured individual qualitative interviews with 20 students who had completed an advanced writing course.The course discussed plagiarism and APA 7 th edition extensively.The participants represented ten countries and ten first languages.One 60-minute interview per participant was conducted online.The data were analyzed qualitatively for recurrent themes.Research findings indicate that the predominant cause of the participants' challenges was their lack of experience using citations before entering the university.Thus, the participants found APA 7th edition hard to observe initially and paraphrasing an enormous challenge.Based on the participant perspectives and related literature, the paper proposes a strategy to implement from the semester start comprising: (1) interactive training workshops with explanations, models, templates, resources, and student practice with citations and academic writing, (2) access to self-correction software like Turnitin and Grammarly Premium, and (3) simultaneous oral-written teacher feedback (Hu, 2019).
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".