An exploratory mixed-methods study on student-athletes' motivation for assessment in sport and academic settings
Bibliographic record
Abstract
Student-athletes in university undergo assessments in both sport and academic domains, which can encompass varying conceptions and outcomes related to assessment. However, questions on whether this ‘doubling up’ of assessments result in similar or different assessment-related outcomes, or whether assessments are conceived the same way across sport and academic contexts, are an omission in achievement research. This study sought to explore the experiences of Canadian student-athletes’ conceptions of assessment, perceptions of control, and emotions in sport and academia through an explanatory mixed-methods design. The study comprised 77 Canadian USports university athletes (Mage = 20.21) for the quantitative data, and 6 athletes partaking in focus group/individual interviews for the qualitative data. The quantitative findings revealed student-athletes reported higher conceptions of assessment as fun and irrelevant in sport compared to university, and greater emotions such as anger, helplessness, and relief in university compared to sport (p < .05). In the qualitative strand, three themes were identified for conceptions of assessment: function, discrete outcomes, broad consequences; three themes for perceptions of control: effort, preparation, and motivation; and three themes for emotions: anticipatory, retrospective, and relational. Mixed insights revealed the importance of assessment consequences, the natural motivation and effort for sport assessment, and the differences in positive and negative emotions between sport and academic domains. Recommendations are discussed for both postsecondary coaches and instructors to help improve sport and academic assessment in ways tailored to the student-athlete experience of assessment.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".