A Teaching Styles Typology of Practicing Teachers
Bibliographic record
Abstract
This study describes a typology of the teaching styles of practicing teachers. Teaching style refers to a teacher’s distinct qualities that are persistent from situation to situation regardless of the content. The Teaching Style Assessment Scale, which measures teaching style, was completed by 1,261 nursing faculty in Japan. Prior cluster analysis revealed four naturally-occurring groups based on their teaching style, with teachers practicing learner-centered and teacher-centered styles nearly equally. The dual purposes of this study were (1) to measure the differences between these groups to determine if they were unique and (2) to describe any teaching style differences among these groups. Analysis of variance and Scheffé descriptive statistics supported the conclusion that a typology exists for classifying teaching styles as they are practiced in the field by teachers. Discriminant analysis identified the distinctions among the groups. The four distinct groups clearly described in this Teaching Styles Typology of Practicing Teachers are Distinctly Teacher-Centered, Moderately Teacher-Centered, Consistently Learner-Centered, and Decidedly Learner-Centered. This typology of teaching styles can be a valuable tool for teachers to improve their professional practice. It has implications for increased professionalism by facilitating increased self-awareness and assisting in developing an educational philosophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".