Teaching Style and Educational Philosophy in Japan: Relationship and Validation of a Teaching Style Typology
Bibliographic record
Abstract
This study examined the relationship between educational philosophy and teaching style among 979 Japanese nursing educators, using two validated instruments: the Teaching Style Assessment Scale (TSAS) and the Learning & Educator Nurturing Style (LENS) inventory. The study addressed four research questions focused on group differences, distinct teaching profiles, and the replication of a previously identified four-cluster teaching style typology. Results from a one-way ANOVA revealed two distinct subsets of educational philosophy—Idealism/Realism and Progressivism/Humanism—aligned with teacher-centered and learner-centered approaches, respectively. Discriminant analyses provided distinct teaching style profiles for each philosophy, confirming consistent and interpretable differences in instructional behavior. A hierarchical cluster analysis replicated the four-cluster typology of teaching styles previously established in a separate national study, further validating its stability. These findings confirm a robust and replicable connection between educational beliefs and teaching practices and provide educators and researchers with a practical framework for reflection and development. The study concludes with a discussion of the implications for faculty development, educational policy, and future research, including the planned development of a short-form teaching style inventory. The project also reflects the responsible integration of Human-AI collaboration during the research and writing process.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".