Diagnostic evaluation in physical education teaching process: A transpositive issue for learning
Bibliographic record
Abstract
This study analyzed the results from the diagnostic evaluation and the choices transpositive induced during the implementation of official prescriptions in a situation of class. The composite theoretical anchoring borrowed for this purpose is inspired by concepts federated by the model of evaluation of Godbout (1988) and the anthropological theory of Chevallard's didactics (2018). The Godbout model (1988) allowed to analyze the measuring instruments used by teachers in connection with their judgment on motor skills and performance carried out by the students Then approach the decisions taken at the didactic level. In addition, through the anthropological theory of Chevallard's didactics (2018) we appreciated the reasons that found the transpositive choices made by teachers. According to the results, teachers who took into account information from the diagnostic assessment in their planning have shown their epistemological relationship and their professional experiences in their practice. It follows from the transpositive choices which favored the acquisition of knowledge and know-how by their students in the APS teaching objects. In contrast, those who have not taken into account the data collected in diagnostic evaluation are subject to official prescriptions and do not often manage to adapt to the needs of students and the requirements in terms of transpositive choices.
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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 | 0.070 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".