Relations Between Epistemic Beliefs and Instructional Approaches to Teaching Games in Prospective Physical Educators
Bibliographic record
Abstract
The academic advantages of knowledge are often compromised when learners and teachers hold counterproductive epistemic beliefs, that is, personal convictions about the nature of knowledge and knowing. This study was an investigation of associations between prospective physical educators' game-specific epistemic beliefs (worldviews and beliefs in simple and stable knowledge and the need for cognition) and their preferred instructional model (Direct, Teaching Games for Understanding, or Sport Education) for teaching games in physical education. Following theoretical and applied instruction of each instructional model, 311 junior and senior undergraduate students enrolled in elective formal games classes at a mid-sized public university in south-central Canada completed several previously validated quantitative measures and one open-ended qualitative item. Those reporting a high need for cognition in games had higher contextualist and lower realist worldviews, along with more sophisticated beliefs about the simplicity and stability of knowledge. Participants most preferred a contextualist epistemic worldview and the TGfU instructional method, and a significant main effect difference was found in epistemic beliefs by preferred instructional model. Students' qualitative comments about each instructional model also reflect a collective awareness of the dynamics of the models and their potential advantages and disadvantages in particular learning settings. It appears useful to increase knowledge about the potential role of epistemic worldviews and beliefs on games teaching preferences in prospective physical educators, particularly if these educators are to foster availing epistemic beliefs in their students.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".