The Kinesiology Curriculum: Using Student Responses to Evaluate Course Content
Bibliographic record
Abstract
The academic discipline of kinesiology is relatively new (1960s) and is devoted to the study of human movement. The knowledge base and subject of study continues to develop and now includes inquiry regarding the impact of physical activity on health, society, and quality of life. The purpose of this course content evaluation was to identify kinesiology students’ views on the addition of public health content in the curriculum. A lecture addressing the background history of public health, with supported reading, was given to forty students enrolled in an upper level Bachelor of Kinesiology-health course. Students were advised of the inclusion of this topic on the final exam and that their responses would be used to inform future direction of the course material. Students were asked to provide their opinion, through a written response to the question: Does public health belong in the Faculty of Kinesiology? A content analysis was employed and seven themes were identified from students’ responses. The themes highlighted professional experience, enhanced leadership, and health promotion and physical activity’s contribution to health. In summary, students positively evaluated the inclusion of public health within the kinesiology curriculum and perhaps this is not surprising given the scoring associated with the question. Despite this limitation, students provided insightful responses worthy of reflection that should be considered in a dialogue about the inclusion of public health content in the kinesiology curriculum.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".