Exploring lesson study in postsecondary education through self-study
Bibliographic record
Abstract
Purpose The purpose of this collaborative self-study inquiry was to enhance the professional practice of faculty members through the adoption of lesson study. A seven-member faculty of education self-study group engaged in lesson study in a computer and learning resources for primary/elementary teachers’ course with teacher candidates. Design/methodology/approach This study focused on providing teacher candidates with increased opportunities for action and expression during in-class instruction. This collaborative lesson study inquiry (Fernandez et al. , 2003; Fernandez and Yoshida, 2004; Murata, 2011) involved the four-step process of planning, doing, checking and acting (PDCA) (Cheng, 2019). Several data collection methods were adopted and data sources analyzed. Findings Challenges the group encountered during the study included ascertaining the goals of lesson study and offering critical feedback to each other. While this made decision-making more intricate and intentional, there was exceptional value in participating in the lesson study process. The results revealed three overarching themes: 1) challenges in classroom observations; 2) hesitation in providing supportive feedback to colleagues and 3) deliberations regarding what constitutes expertise within subject-specific preservice teacher education. Originality/value While lesson study has been adopted fairly extensively in K-12 settings, its adoption in postsecondary education is limited (Chenault, 2017). Considering the merits of lesson study for K-12 practitioners, this research investigated the similar advantages that lesson study might have for postsecondary education faculty, students and programs.
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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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".