Enabling the uptake of pedagogical innovations in physical education: The role of social media
Bibliographic record
Abstract
Despite the development of many promising innovations in physical education (PE), their lack of sustained use by teachers often stems from inadequate access to effective professional development (PD). Social media has the potential to serve as a platform for accessible, \nself-driven PD, supporting teachers’ ongoing learning about pedagogical innovations. The purpose of this research is to investigate the role that social media has played in enabling teachers’ uptake and sustained use of the pedagogical innovation Meaningful PE. The research is grounded in Wenger's (1998) social learning theory, which positions learning as a social process occurring through active engagement and participation within a Community of Practice (CoP). \nQualitative data were gathered from six teachers through individual semi-structured interviews. The findings reveal that social media served as a catalyst for continuous learning and reflection among PE teachers, and facilitated their uptake of Meaningful PE. The presence of experts and peers on social media platforms provided a dynamic environment for professional learning, growth, collaboration, and innovation. The formation of a CoP consisting of researchers, practitioners, and teachers on social media significantly influenced the teachers’ sustained implementation of Meaningful PE. By engaging in discussions, sharing experiences, and collaborating online, teachers were able to maintain their implementation of Meaningful PE. This collaborative, reflective, and sustainable environment aligns with the essential characteristics of effective PD, recognizing teachers as active participants within a supportive social context. \nOverall, this research provides evidence of the role that social media can play in supporting teachers’ uptake and sustained implementation of pedagogical innovations. Implications are considered for PD programs and their providers.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".