Bibliographic record
Abstract
Purpose This study aims to explore the roles of Koshi within the Learning Study framework, a Hong Kong adaptation of Lesson Study guided by variation theory. It examines Koshi’s pathways to becoming mentors, facilitators and critical friends, their day-to-day responsibilities and how they refine their practices through educational interventions. Design/methodology/approach A narrative inquiry approach was employed, focusing on two Koshi: a practice-driven Vice Principal (VP) and a research-driven University Lecturer (UL). Data were collected through semi-structured interviews. Cross-case analysis was used to identify common themes and compare their roles, strategies and contributions to teacher learning and professional growth. Findings The study reveals that Koshi play multifaceted roles, including mentors, facilitators and knowledgeable others, bridging theory and practice to support teacher collaboration and reflective inquiry. Leadership support, structured professional development and a culture of knowledge-sharing are critical to their growth. Koshi learn by doing and refining their facilitation techniques through real-world practice and peer feedback. Research limitations/implications The small sample size and Hong Kong-specific focus limit generalisability. Further research across diverse contexts is needed to validate findings. Practical implications Structured training programmes, leadership support and peer collaboration are essential for nurturing Koshi’s capacity to drive teacher professional development. Social implications Koshi foster a culture of reflective practice and collaboration, contributing to sustainable educational improvement and teacher empowerment. Originality/value This study provides new insights into the dual roles of Koshi as both practice-driven and research-driven facilitators in Learning Study. It highlights their impact on fostering professional learning communities and bridging theory with classroom application.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".