Factors influencing sports science students’ elective biomechanics enrolment decisions
Bibliographic record
Abstract
The modularisation of sports science curricula allows students to individualise degrees to fit their interests and aspirations via elective modules. The aim of this study was to explore the factors which influence sports science students' elective biomechanics enrolment decisions. A total of 45 students completed an online survey focussing on personal and academic characteristics which may influence enrolment decisions. Significant differences were found for three personal characteristics. Biomechanics module enrolees were more positive in their self-concept of subject ability, had a greater like for their previous subject experience, and displayed a higher agreement in requiring the knowledge for future career aspirations. Although, statistical power was reduced when respondents were categorised into demographic sub-groups, exploratory analysis highlighted self-concept of subject ability may differentiate female students' enrolment, while previous subject experience may distinguish male students' enrolment and academic entry route students' enrolment. Undergraduate sports science core biomechanics modules should consider adopting learning pedagogies which help to increase individual students' self-concept of ability and inspires them to recognise the value of biomechanics in their potential career aspirations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".