Unpacking the motivational variables which impact engagement in Lesson Study: Mathematics teaching self-efficacy and attitudes towards self-development
Bibliographic record
Abstract
Lesson study has received significant attention as a model of professional development among mathematics teachers. Evidence highlights its effectiveness in improving pedagogical practices and student learning, however, less is known about the predispositions which may encourage teachers’ participation in Lesson Study or the impact of participation on teachers’ attitudes. Such findings are relevant considering the voluntary context of teachers’ participation in professional development in Ireland.This research investigates the motivational variables which impact teachers’ participation in Lesson Study, specifically their self-efficacy in teaching mathematics for conceptual understanding and their attitudes towards self-development in Lesson Study. Post-primary mathematics teachers (N = 64), spanning various levels of experience in Lesson Study, completed a survey using a set of pre-validated scales. Findings indicate that teachers’ mathematics teaching self-efficacy is a significant predictor of their participation in Lesson Study. Furthermore, the research finds that teachers’ familiarity with Lesson Study impacts the likelihood of their participation in this model of teacher education.These findings build upon previous knowledge in this field and demonstrate the significance of teaching self-efficacy as a presage variable for developing a positive disposition towards Lesson Study. The paper discusses the implications of these findings for teacher education in Ireland.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".