Academic Dishonesty in Online Assessment from Tertiary Students’ Perspective
Bibliographic record
Abstract
Typical face-to-face assessments were suspended by many tertiary institutions since the first quarter of 2020 due to the pandemic of COVID-19. Many have resorted to online assessment to evaluate students’ performance. However, academic dishonesty particularly plagiarism becomes an issue as the technology utilized to complete the online assessment provides students with the opportunities to commit academic cheating. Hence, the objectives of the study are to explore the potential methods employed by tertiary students to cheat in online assessment and identify the preventive measures taken by lecturers and tertiary institution to curb the problem. A survey was conducted in a tertiary institution with 403 responses were obtained to achieve the objective of the study. The results show that five of the potential methods were not employed to cheat in online assessment as perceived by the tertiary students, while they moderately agreed on the other three. This is probably due to the three preventive measures implemented by the tertiary institution to curb the problem. However, these measures should be well-implemented to ensure its effectiveness in preventing academic cheating in online assessment.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".