ESL Student Plagiarism Prevention Challengesand Institutional Interventions
Bibliographic record
Abstract
Research has found unintentional plagiarism to be the most common type of university plagiarism, yet what underlies it is not adequately understood. Thus, our study examines ESL student perspectives on academic integrity challenges, especially unintentional plagiarism and university interventions. The study employed semi-structured individual qualitative interviews with 20 ESL students who had just completed an advanced EAP writing course at a Canadian university in the Winter semester of 2021. The course discussed plagiarism and the APA 7th edition extensively. One interview per participant was conducted online and the data were analyzed qualitatively. Research findings indicate that the predominant cause of the participants' challenges was their lack of experience using citations before entering the university. The participants had written no formal essays or only opinion-based essays without source requirement. Therefore, the participants found the APA 7th edition hard to observe initially. They all found paraphrasing a challenge. A less serious one was to create a reference list of various types of sources in APA 7. Regarding assistance, the participants felt that the style templates and models were valuable, but, that even more, so were the interactive workshops at the semester's start. Thus, a combination of resources, workshops, and teacher-facilitated practices, along with improved writing are expected to empower ESL students (Khoo, 2021).
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".