Policy Review: Academic Cheating in Online Examinations during the COVID-19 Pandemic
Bibliographic record
Abstract
With the liberalization policies towards the COVID-19 pandemic in various countries, in-person teaching is the mainstream currently. Many countries are more open to their border and quarantine/isolation requirements; however, this does not mean the contagious virus is gone. Various variants are still a threat to people’s health. Online teaching has its essential to students. Our observation was based on the almost three-year pandemic experience towards the widely used online teaching, especially on academic cheating behaviors during examinations. We found that the online teaching during the COVID-19 pandemic facilitated students to obtain high scores through improper cheating in online examinations. Academic faculty faces a big challenge when they try to use new technologies to protect the integrity of online exams because students can develop new strategies for cheating. They not only surf the internet to find the correct answers but also get help from peers and experts. Cheating prevents students from gaining essential skills and knowledge. It is unfair to honest students who spend time and effort studying course materials. In this article, we explored the common ways of cheating on online exams. We also provided recommendations to prevent academic misconduct in the digital environment.
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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 | 0.039 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.033 | 0.018 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".