Editorial: Global lesson study policy, practice, and research for advancing teacher and student learning in STEM
Bibliographic record
Abstract
Last year, we celebrated the 25th anniversary of The Teaching Gap (Stigler & Hiebert, 1999). In this issue, we reflect on the global impact of Lesson Study as a professional development approach for teacher learning. Over the past decades, Lesson Study has expanded beyond its origins in Japan and has been adopted in diverse educational settings worldwide. This special issue of Frontiers in Education (STEM Education) brings together cutting-edge research that examines the policy, practice, and research dimensions of Lesson Study, with a particular emphasis on STEM education.Lesson Study fosters collaborative and reflective teaching practices centered on student learning. After 25 years, it remains a vital method to support pre-service teacher education (e.g., Gomes, et al., 2022;Helgevold & Wilkins, 2019), in-service teacher professional development (e.g., Andriano & Manolino, 2023;Clivaz et al., 2023), and team collaboration of teacher learning with mixed teaching experiences (Coenders & Verhoef, 2019). While numerous studies have explored Lesson and Learning Study in STEM education across the globe (e.g., Arzarello et al., 2022;Brown-Tess, 2021;Clivaz & Miyakawa, 2020;Groves et al., 2016;Huang & Shimizu, 2016), the adoption of this professional development process differs from the ways it is experienced in Japan. Despite its widespread use, variations in implementation have prompted discussions on its fidelity and efficacy across cultural and institutional contexts (Brown, 2024;Fujii, 2018;Capone et al., 2023). A critical aspect is how Lesson Study interacts with diverse educational cultures. Research highlights the crucial role of cultural influences in shaping teacher reflection and instructional change, underscoring their significance in mathematics teacher education (Manolino, 2024). This perspective reinforces the need to contextualize Lesson Study within specific educational traditions to ensure its meaningful and sustainable application. This special issue presents a collection of high-quality studies that contribute to the discourse on Lesson Study, emphasizing its impact on teacher professional development, student learning outcomes, and pedagogical innovation in STEM education. As the field moves forward, it is essential to maintain a dialogue between researchers, educators, and policymakers to ensure that Lesson Study continues to evolve as an effective, evidence-based approach to advancing STEM education. We hope that this special issue provides valuable insights and inspires further innovations in teaching and learning through Lesson Study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".