Learner-centered teaching in a Higher Education course: a case study with a recognized researcher in Sports Coaching
Bibliographic record
Abstract
Sport coaching researchers have provided generous recommendations on the importance of developing coach education programs based on learner-centered teaching (LCT) principles. However, empirical studies are rare, and without concrete examples, administrators and instructors will be reluctant to adopt this approach. In this case study, we used Weimer’s (2002, 2013) five dimensions of LCT to analyze (a) the perspective of a recognized researcher in the LCT coach development field, (b) his course plan and delivery strategy, and (c) the students’ perceptions of this course. A qualitative approach was used and included different tools to collect the data. The first two authors attended all the lessons, participated in all the learning activities, and took notes in a reflective journal. At the end of the semester, they conducted a semi-structured interview to get the instructor’s perspective on the LCT approach. Finally, an e-mail was sent to the students to collect their perceptions. The data were analyzed and interpreted using concept mapping, and Weimer’s five dimensions. The results showed that (a) most of the LCT dimensions were respected in the planning and delivery of the course, (b) there were times when the instructor felt uncomfortable playing a less important role, and (c) most students had positive learning experiences, although some have been taken out of their comfort zone with this new teaching approach. The article ends with a reflection on the recent impact of COVID-19 on education in Higher Education (HE).
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".