Learning pains: practical considerations in migrating exercise physiology labs to a virtual environment during the COVID‐19 pandemic
Bibliographic record
Abstract
The COVID‐19 pandemic provided an unprecedented challenge for physiology instructors as previously in‐person course offerings were required to migrate to a virtual environment. In particular, the migration of exercise physiology labs, which included anaerobic and aerobic exercise tests, posed a considerable hurdle in attempting to provide a practical and engaging lab environment fully online. Using a qualitative case‐study design, this presentation will highlight the experience of migrating exercise physiology labs into a virtual post‐secondary course context. In Fall 2020, approximately 200 Kinesiology students attended a virtual second‐year exercise physiology course, which included four previously in‐person, bi‐weekly labs. Labs were rapidly migrated onto an open access platform for integration into the existing institutional learning management system. Students completed bi‐weekly labs in lab groups of 20‐25 students, led by a Teaching Assistant (TA), with students working in small breakout groups of 4‐5 students to complete the virtual lab as a group using a collaborative workspace. Following the breakouts, students would rejoin their peers in the main group for a post‐lab discussion period to discuss lab report questions with the TA. After the lab, students completed a content quiz which included a responsive question: “What did you like about the lab? What do you feel could be improved about the lab?” Given the importance of considering students as partners in course development, responses from students were considered in refinement of future virtual labs throughout the term. Responses were analyzed using qualitative coding for positive/neutral/negative responses, and general themes emerged for each lab. Briefly, main themes for improvement included: increased organization and instruction for navigating the virtual lab, more contact with the TA in breakout rooms, improving engagement between members of breakout groups, and enhancements to the virtual lab components. Positive themes included: students enjoying breakout room opportunities to connect with peers, TA support especially in the post‐lab discussion period, and additional cues added to the virtual lab. Finally, student responses became increasingly positive from the first lab to fourth lab, with students noting their appreciation for being a part of the refinement process. Overall, this presentation, detailing the practical considerations of migrating labs to a virtual environment, will benefit future exercise physiology instructors in pursuing successful virtual lab delivery.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".