An Activity for Building Teaching Potential Designed on Community of Practice Cooperated with Lesson Study
Bibliographic record
Abstract
This paper proposes the development of an activity based on the community of practice (CoP) approach in collaboration with lesson study to enhance teaching potential. The CoP approach is utilized to elicit teachers' experiences and facilitate the sharing of teaching guidelines, while the lesson study method enables small groups of teachers to collaboratively design, teach, reflect on, and refine a class lesson. Drawing from semi-structured interviews, classroom observations, documentation, expert field notes, and focus groups, the proposed activity consists of four key components: 1) principle, 2) activity objective, 3) learning activity, and 4) learning evaluation. The learning activity encompasses four steps: educating, innovating, implementing, and reflecting. Each step comprises several sub-activities, with the innovating and implementing steps being iterative. The activity demonstrates a content validity of 0.95 and a suitability rating of 4.88. Furthermore, the participating teachers in this study exhibit increased self-confidence in constructing classroom activities and gained additional pathways for designing effective learning activities. The paper suggests that this approach can effectively foster the acquisition of new knowledge, the development of innovative practices, and the application of effective instructional strategies in the classroom.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".