Regulation of tactical learning in team sports – the case of the tactical-decision learning model
Bibliographic record
Abstract
Background: Several student-centred and game-based approaches have developed in the last 40 years. Publications intended to describe the underlying theory and/or mechanics of each particular teaching/learning model have usually focused on modalities related to teacher-student interactions, taking into account the particular pedagogical content knowledge pertaining to the activities concerned. The extent to which a particular approach has students taking charge of their learning varies from one model to the other. Underlying learning theories, such as constructivism and nonlinear pedagogy, play an important role in explaining or justifying reasons for pedagogical choices. However, whatever approach is favoured by a teacher, there remains the matter of the regulation of learning in a student-centred teaching/learning environment.Purpose: The main purpose of this paper was to discuss the regulation procedures implemented in a particular team-sport related teaching/learning strategy called the ‘Tactical-Decision Learning Model’ (T-DLM). A second and preliminary objective was to examine various AfL models with regard to the regulation of learning.Scaffold of the paper: In the first section of the paper, a review of the literature is presented with regard to the diversity of the student-learning regulation construct, spreading from self-regulation to co-regulation and to shared/socially-shared regulation. In the second part of the paper, the authors discuss the regulation of tactical learning in team sports through the lens of T-DLM, considering the contribution of four basic features of the model: game-play in a small-sided game format, student observation, debate, road map, and their iterations.Conclusion: In a team-sport teaching/learning context, cooperative learning becomes, by definition, the pivotal characteristic of the learning and learning-regulation processes. Although ever present, self-regulation is intermingled with socially shared regulation in the sense that each student’s self-regulation activities voluntarily mingle with that of his/her teammates to bring about a collective action plan. Due to its socio-constructivist foundation and particular features, T-DLM offers many opportunities for socially-shared regulation of learning and may, given the right conditions, open the way for socially-shared metacognitive awareness of learning-regulation processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".