Making Sense of Decision Making in Invasion Team Sports - A Teaching/Learning Perspective in Physical Education
Bibliographic record
Abstract
Since the 1990s, decision making (DM) in sports has been extensively investigated, particularly through expert players' decisions made mostly in standardized contexts but also, to a lesser extent, in naturalistic settings. The purpose of this article is to re-examine the teaching/learning of decision making in invasion team sports in light of the contemporary research conducted with high-level performers. First, following a brief overview of the situation awareness (SA) construct, three decision making (DM) perspectives are presented: information processing (IP), naturalistic DM, and ecological dynamics (EcoD). In a second major section, invasion-team-sports (ITS) SA in PE is examined with regard to SA components and the differentiation of five SA facets. In a third major section, presenting implications for ITS DM learning in PE, the teaching/learning of ITS-DM is discussed with regard to beginner- and novice-level players in Physical Education. Constructing a shared reference-framework for DM through team reflection on game-play situations is also considered, namely with regard to critical-incidents analysis and unexpected play-occurrences. In a context of the teaching/learning of DM in ITS in school, the authors submit that precedence should be given to information processing and to recognition-primed perspectives. Resort to mental representation networks and recognition of familiar configurations of play is critical to establish situation awareness and learn to make appropriate decisions. Such an option fits well with a social constructivist view of DM learning. Keywords: invasion team sports, decision making, situation awareness, information processing, recognition-primed process
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".