Transition from Face-to-Face Teaching to Online Teaching in Times of Pandemic
Bibliographic record
Abstract
The education systems around the world have had to adapt quickly to find ways to offer university programs remotely in the face of the Covid 19 pandemic. Without the proper preparation and sufficient knowledge, instructors learned to teach online during the Winter 2020 semester to allow students to complete their courses when campuses suddenly closed as a safety measure. Normally many weeks of preparation would have been necessary to redesign a course to be efficiently offered online. This paper studies some aspects of the transition from Face to Face teaching in the Fall 2019 semester to Face to face / Online teaching in the Winter of 2020 (beginning of the pandemic) to completely Online teaching in the Fall 2020. This transition is examined in a Managerial Analytics course offered in the first semester of an MBA program at a Canadian University. A survey administered at the end of each semester reveals different levels of students’ anxiety, modification in the communication tools utilized, changes in intensity of weekly study hours and expected recollection of the material learned in the course, a year after completion. Additional variations are also observed by gender.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".