Learner-Centeredness vs. Teacher-Centeredness: How Are They Different?
Bibliographic record
Abstract
This study describes the teaching style behaviors that differentiate between learner-centered and teacher-centered approaches to teaching. The Teaching Style Assessment Scale, which measures teaching style, was completed by 1,261 nursing faculty in Japan. Discriminant analysis and cluster analysis revealed that the distinctive characteristic distinguishing the learner-centered approach from the teacher-centered approach is Personalizing Instruction. Personalizing Instruction recognizes and utilizes the uniqueness of each student’s strengths. Personalizing Instruction can facilitate students’ interpersonal understanding and self-awareness. By implementing Personalizing Instruction, teachers can facilitate the metacognitive process in their students, which is healthy for the individual and productive for meaningful learning. Learner-centeredness embraces Personalizing Instruction, while teacher-centeredness rejects it. Teachers practice learner-centered and teacher-centered styles nearly equally. Personalizing Instruction is the most critical teaching style element and the indispensable definitive factor separating these styles.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".