Student and instructor perceptions of engagement after the rapid online transition of teaching due to COVID‐19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it