Transition to online learning during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: With the new pandemic reality that has beset us, teaching and learning activities have been thrust online. While much research has explored student perceptions of online and distance learning, none has had a social laboratory to study the effects of an enforced transition on student perceptions of online learning. PURPOSE: We surveyed students about their perceptions of online learning before and after the transition to online learning. As student perceptions are influenced by a range of contextual and institutional factors beyond the classroom, we expected that students would be overall sanguine to the project given that access, technology integration, and family and government support during the pandemic shutdown would mitigate the negative consequences. RESULTS: Students overall reported positive academic outcomes. However, students reported increased stress and anxiety and difficulties concentrating, suggesting that the obstacles to fully online learning were not only technological and instructional challenges but also social and affective challenges of isolation and social distancing. CONCLUSION: Our analysis shows that the specific context of the pandemic disrupted more than normal teaching and learning activities. Whereas students generally responded positively to the transition, their reluctance to continue learning online and the added stress and workload show the limits of this large scale social experiment. In addition to the technical and pedagogical dimensions, successfully supporting students in online learning environments will require that teachers and educational technologists attend to the social and affective dimensions of online learning as well.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".