Online Absenteeism and Strategies for Optimising Students’ Participation During COVID-19-Induced Online Learning
Bibliographic record
Abstract
Since the last quarter of 2019, the COVID-19 outbreak has spread rapidly around the world which prompted the World Health Organization to declare it a public health emergency and, then, a global pandemic. To reduce the impact of the pandemic, many countries, including Zimbabwe, adopted strategies based on social distancing rules and stay-at-home lockdowns. These strategies had severe disruptive consequences on many sectors, including all levels of education. In the case of the education sector, traditional face-to-face teaching was replaced with online teaching and learning. Although online learning was a necessary intervention which ensured that students continued with learning, it poses some ethical challenges related to genuine participation with regard to both class participation and the completion of assigned exercises, quizzes and tests among other methods of assessment. Put differently, when students are part of online classes, it becomes difficult to tell whether they are in class or not. When using Zoom or Google Meet for instance, students may not have an obligation to unmute their audio and video tools during classes. Second, when assignments and tests are administered and completed online, it is difficult to tell whether students have honestly completed the assigned work on their own or whether third parties were involved.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".