Investigating the relationship between emotions and physical literacy in a quality physical education context
Bibliographic record
Abstract
Affective experiences have been shown to influence attitudes and future behaviors related to physical activity, but no studies have related these experiences to physical literacy or examined affective experiences in quality physical education (QPE). The aim of this study was to describe emotions experienced in a QPE context and relate those emotions to students’ physical literacy and self-esteem. A cross-sectional study of 145 4th and 5th grade students was surveyed after a single PE class on their emotional experiences (positive emotions: pride, enjoyment, and relief; negative, emotions: shame, anger, and boredom; aggregate emotional experience as the sum of positive and negative emotions) physical literacy (self-description and movement valuation) and self-esteem. In this QPE context, 73% of students reported only positive emotions, 26% mixed positive and negative emotions and 1% expressed only negative emotions. Pride and enjoyment were strongly co-occurring (89%). Among students expressing mixed emotions, 90% still yielded positive aggregate emotional experiences. Positive emotions have significant ( p < 0.001), moderate to strong positive correlations with physical literacy (rho = 0.65 pride, 0.50 enjoyment) and self-esteem (rho = 0.48 pride, 0.38 enjoyment), and negative emotions have significant ( p < 0.001), moderate negative correlations with physical literacy (rho = −0.47 shame, −0.30 anger, −0.32 boredom) and self-esteem (rho = −0.33 shame, −0.29 anger, −0.21 boredom). This study reveals strong positive emotional responses by students in a purported QPE context. The presence of mixed emotions with net positive aggregate experiences highlights the importance of consideration of both negative and positive emotions in movement contexts. Affective states have been proposed as key elements of physical literacy, and this study supports this through valence matched associations between both positive/negative affect with physical literacy and self-esteem.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".