Lessons Learned from Faculty and Students' Perceptions of Remote Learning
Bibliographic record
Abstract
Remote learning brought with it challenges for both learners and educators alike. While some in higher education are keen to return to “how things were,” we believe that remote learning brought with it lessons that should be implemented into post-pandemic teaching. We sought to investigate what components of online/blended learning approaches both learners and educators would like to see continue in a post-pandemic era, as well as identify supports needed for faculty to successfully implement online/blended learning. Using a sequential explanatory mixed-methods approach, we collected data from students and faculty across one School in a variety of programs (undergraduate, graduate, professional). Using thematic analysis and descriptive statistics, we found that while remote learning was challenging in terms of educator/learner engagement and social isolation, both educators and learners identified that remote learning helped with the development of time management skills, and flexibility in schedule planning. Further, educators noted the benefits of adopting a blended learning approach post-pandemic, such as providing for more purposeful in-class activities and increasing accessibility. Overall, our work contributes to the scholarly discourse of post-pandemic teaching and learning, and provides key insights into how to best support educators and learners.
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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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".