Academic Continuity: Staying True to Teaching Values and Objectives in the Face of Course Interruptions
Bibliographic record
Abstract
Academic continuity planning is an emerging tool for dealing with class cancellation associated with natural disasters, acts of violence and the threat of pandemics. However, academic continuity can also be an issue with respect to less dramatic events, such as power outages, inclement weather, or the temporary unavailability of an instructor, especially if the problem is recurring. Many of the proposed alternative forms of delivery involve some form of web-based learning, but the extent to which these approaches work when students expect face-to-face delivery has not previously been examined. In one such interruption, web-based conferencing from home was undertaken. Based on average test scores, learning was unimpeded by web-based conferencing for one week, but there were some small gender effects that warrant further investigation. Many student comments reflected reduced engagement. The professor noted that students were more likely to respond to questions when the students could see the professor instead of the slides, but in general there were fewer student responses to questions than in face-to-face lectures. A number of comments made unsolicited comparisons with the traditional lecture format, suggesting that the context for teaching and learning, and students’ previous experience of different teaching approaches may merit more discussion in online learning studies.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".