Challenges with Online Teaching and Learnings for the Post-Pandemic Classroom
Bibliographic record
Abstract
Abstract At the start of 2020, safety concerns stemming from the COVID-19 pandemic caused educational institutions around the world to rapidly transition to emergency remote learning (ERL). This has caused educators to rethink their course delivery strategies and re-examine their assumptions about what constitutes a good education. Although the research community has widely reported on remote learning—including its benefits, its challenges, and suggestions for the future—institutions have recently begun resuming in-person activities, which begs the question, what has changed? While previous work has compared remote learning during the pandemic to pre-ERL in-person learning, we expand on the findings of the community by examining student feedback obtained during post-ERL in-person learning. We begin by discussing the main challenges we faced during the year of online teaching, then present an analysis of survey data gathered for both remote and (post-ERL) in-person learning during the pandemic. Insights on synchronous and asynchronous learning are presented, including the benefits and drawbacks that are unique to each. We show that while students generally preferred synchronous learning over asynchronous, many of the key benefits of synchronous learning are only attainable in a physical setting. We discuss the reasons for this, as well as the reasons why students overwhelmingly desired an asynchronous learning option to augment their synchronous learning activities. Unlike many previous studies which solely rely on quantitative survey data, we draw our conclusions using a combination of quantitative data and written feedback from students, the latter of which allows us to better understand students' reasons for adopting certain learning strategies and preferences. Alongside these insights, we identify opportunities for improving student satisfaction and share actions we took to better support our students..
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".