Faculty perceptions of the exercise science student learning experience during the coronavirus pandemic
Bibliographic record
Abstract
Significant disruptions in higher education course delivery occurred during the coronavirus (COVID-19) global pandemic. The implementation of emergency remote teaching (ERT) offered exercise science faculty a safe method to continue educating students in courses generally taught face-to-face. The purpose of this investigation was to explore faculty perceptions of their ERT efforts with respect to student successes, challenges, and faculty expectations. Through an electronic survey, participants ( n = 112) from higher education institutions in 31 states and three Canadian provinces provided feedback on their perceptions of the student experience across 315 fall 2020 courses. Data analysis included a thematic analysis to identify themes and trends in participant responses. Faculty identified student adaptability, increased autonomy of learning, and maintenance of learning as successes. Also noted was the increased flexibility of alternative pedagogy methods. Participants perceived student challenges related to technology, time management, and well-being. Faculty perceived students expected increased accommodations and instructor responsiveness during fall 2020. While faculty and students were challenged to adapt during the global pandemic, the perceived ERT experiences during COVID-19 highlight the resiliency of higher education students and underscores changes needed by educational institutions to provide resources and training upon return to traditional education or in response to a future crisis.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".