Accompanying Learning in Dance Classes in Quebec Schools through Movement Observation Analysis (MOA)
Bibliographic record
Abstract
As a school discipline in Quebec, dance is heir to the Modern Educational Dance (MED) movement, founded by Rudolf Laban (1948, 1976), better known in Quebec as creative dance (Raymond, 2014, p. 22). About ten years ago, Movement Observation-Analysis (MOA) (Harbonnier, Dussault, Ferri, 2021) was introduced into the artistic and pedagogical training of school dance teachers in Quebec. With the aim of revising ministerial programs for teaching dance in Quebec schools, which are over twenty years old, we wondered how MOA concepts were useful to dance teachers in Quebec schools. To this end, we asked ten school dance teachers to describe their use of OAM in their teaching, using the technique of the explicitation interview (Vermersch, 1994). With regard to the pedagogical function of school dance teachers, the results of our research, through the case study of teacher Suzie, show that MOA provides an enriched and clarified conceptual framework and lexicon to refine movement observation activities (diagnostic function), improve pedagogical communication (physical demonstration and verbal instructions), and provide knowledge about movement to share with students, fostering their autonomy in learning (learning support function).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".