“It Feels Weird Telling You For Sure”: Ambivalence and Uncertainty about Academic Integrity in International Students’ Self-Reports of Using Paid Academic Support Services
Bibliographic record
Abstract
This presentation reports on a study that examines some international undergraduate students’ use of private academic support services (PASS), a growing phenomenon in Canadian higher education in the last decade whose practitioners often advertise to international and/or multilingual students. We situate the use of PASS at the intersection of three constructs: literacy brokering (Curry and Lillis, 2006), contract cheating (Lancaster & Clarke, 2016), and private supplementary tutoring/“shadow education” (Bray, 2008), seeking to understand why some students choose to pay for help with academic work and how they understand the ethics of their choices. Using a survey (n = 898 international student responses, with ⅔ being self-identified as users of English as an additional language) and semi-structured follow-up interviews (n= 23), this presentation addresses the following research questions: What is the type and nature of PASS that some students use, and why do they seek such services? How do students understand the use of PASS in relation to academic integrity? Survey results reveal 30% of participants described using some form of PASS, most commonly “homework help” websites or tutoring. Students described a variety of reasons for seeking PASS, often related to their perceived convenience and/or limited access to other forms of in-time support. Interviews revealed ambivalence about the use of PASS; while some reported feeling PASS were helpful in achieving academic goals, some described feeling uneasy or uncertain about their ethical acceptability or usefulness. We conclude with policy and pedagogical implications for this growing grey area of para-academic support.
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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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".