Student Understanding and Learning in Team Sports: Understanding through Game-Play Analysis
Bibliographic record
Abstract
The purpose of this paper is three-fold: (a) to summarily examine the matters of team-sport subject-matter knowledge and student team-sport pedagogical content knowledge learning as they evolved in France since the 1960s, (b) to recall briefly the main constitutive elements of the Tactical-Decision Learning Model (T DLM) and their ties with student understanding and learning, and (c) to illustrate the use configurations of play and effective play-spaces as tools for enhancing student learning. Through T-DLM, students are challenged to collectively plan action projects, implement them in game play situations, and conclude as to their level of success or failure, going through several iterations of the process until stabilization of their acquired knowledge. This learning process unfolds under the teacher’s learned and facilitating guidance. Keywords: T-DLM, team-sport understanding, debate-of-ideas, configuration of play, student-centered approach
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".