Implementing Team-Based Learning to Strengthen Communication Skills among Undergraduate Kinesiology Students
Bibliographic record
Abstract
Kinesiology is the study of human movement and grounded in learning about physiological and psychological mechanisms of physical activity, exercise, and sport. Despite the educational focus promoting an active lifestyle, teaching strategies often ignore the hands-on and interactive components of the field, in favour of a traditional passive teaching style (Bulger, Housner, & Lee, 2008). This teaching approach can be problematic as most undergraduate Kinesiology students will either pursue an academic career path, or enter a health care field (e.g., kinesiologist, medical doctor, physical therapist, etc.) Whichever path a student chooses, it will require strong communication skills, whether it be sharing research ideas or working with a patient. To improve these skills, instructors can use an interactive classroom. A recent study evaluating communication competence in undergraduate nursing students found overall improvements in communication efficacy and communication ability when implementing team-based learning (TBL; Cho & Kweon, 2017). Therefore, a larger focus in Kinesiology should be on promoting effective communication skills so that students are more prepared when they graduate. By incorporating TBL into Kinesiology courses, students can become more interactive in the classroom and build upon fundamental skills that are paramount in academic and health care settings (Meeuswen, King, & Pederson, 2005).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".