Student and instructor perceptions of engagement after the rapid online transition of teaching due to COVID‐19
Bibliographic record
Abstract
Abstract Engagement involves students’ investment in learning activities, as well as interrelated affective (emotive responses), behavioral (active responses), and cognitive (mental effort) components. This study assessed undergraduate student and instructor perceptions of the interrelated components of engagement during and after the rapid online transition of teaching in March 2020 due to the COVID‐19 pandemic. Fifteen courses—including laboratory, discussion‐based, large lecture, tutorial, and problem‐based learning—within a multi‐disciplinary faculty at a large research‐intensive Canadian university were surveyed to: (a) assess student and instructor perceptions of students’ levels of engagement during and after the rapid transition to online teaching due to the COVID‐19 pandemic; (b) describe which aspects of engagement were enhanced or diminished due to the rapid online transition; and (c) identify which learning activities students would find most engaging in an online setting so as to assist in developing student‐centered online pedagogical techniques. Student engagement was lower after the rapid online transition. Students who engaged by connecting with peers and instructors through in‐class discussion (affective engagement) had diminished engagement, whereas students who engaged by listening to lectures, reading course materials, and reviewing slides (cognitive engagement) had enhanced engagement. Overall, students found synchronous activities more engaging. Students experienced positive and negative outcomes related to classroom engagement when transitioning rapidly to online learning during a global pandemic.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".