‘The teacher could correct me without being there’: Adapting distance education approaches to promote physical activity during lockdown
Bibliographic record
Abstract
Despite the impossibility of face-to-face teaching during the covid-19 pandemic lockdown, many physical education teachers used remote activity pedagogical monitoring (PM) to keep students engaged in physical activity (PA). The aim of this study was to explore students’ experiences of remote PM practices during lockdown to engage in PA. A sequential explanatory mixed methods design was used, with a qualitative investigation (students’ experience of PM) informed by a quantitative investigation (relationship between PA and PM as a function of diligence). First, 644 French students (16.32 ± 1.01 years) participated in a longitudinal survey to collect retrospective data about their reported PA levels during a typical week before lockdown and four weeks after. A second step consisted of identifying clusters, based on how PA emerged in participants and diligence in PM. Five clusters were identified from which eight paragons accepted to be interviewed. Interviews were conducted with paragons from each cluster to understand their different lived experiences during PM. Results showed a significant decrease in PA during lockdown, with PM serving to limit the drop-out from PA. Positive experiences in PA engagement were associated with: (a) family and video support, (b) variety in the PA program, (c) requests for work, (d) provision of feedback, and (e) use of personalised training. Results are encouraging in terms of developing hybrid pedagogical practice which includes face-to-face activity and use of PM. Further research is needed to ensure these pedagogical principles lead to positive experiences of PA engagement at a distance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".