Exploring Online Physical Education Teaching: What Have We Done and What Have We Learnt?
Bibliographic record
Abstract
Engaging with physical education teachers who were compelled to integrate technology into their lessons during the COVID-19 pandemic is crucial to understanding how the pandemic has presented this ‘new normal’ circumstance. It is vital to gain insight into the initial experiences of physical education (PE) teachers who transitioned to online physical education (OLPE) teaching, as well as to identify potential areas for improvement in the future. This study investigated the perspectives of secondary school PE teachers on OLPE teaching during the COVID-19 lockdown, their professional development, online training opportunities and future perceptions. Using a mixed-methods approach, this study analysed data from 35 secondary school PE teachers in Fiji, using Google Forms to collect quantitative data and semi-structured interviews for qualitative data. The quantitative data was categorized by age, gender, school setting, qualifications, and teaching experience, while the qualitative data was analysed by themes. The study found that teachers struggled with OLPE due to lack of preparedness, poor Internet connectivity, and lack of emphasis on PE during lockdown. Despite their readiness, integrating technology remains challenging due to a lack of incentives, limited support, and fear of the unknown. The study emphasises the vital importance of technology in creating engaging and relevant PE experiences and recommends the provision of specialised resources, personalised curriculum guidance, and a change in teacher training institutions' paradigms to incorporate contemporary technological applications in PE.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".