Teacher Effectiveness Examined as a System: Interpretive Structural Modeling and Facilitation Sessions with U.S. and Japanese Students
Bibliographic record
Abstract
This study challenges narrow definitions of teacher effectiveness and uses a systems approach to investigate teacher effectiveness as a multi-dimensional, holistic phenomenon. The methods of Nominal Group Technique and Interpretive Structural Modeling were used to assist U.S. and Japanese students separately construct influence structures during facilitation sessions. The influence structures represent maps for understanding teacher effectiveness as a system. The influence maps indicate that there are a number of teacher behaviors and characteristics that promote, support and influence one another within the overall system; however, the plurality of teacher elements, which are structured with priority, concerns teacher knowledge characteristics and verbal teacher immediacy behaviors for both cultural groups. The findings of the study were explored from thematic perspectives in intercultural communication such as power distance, identity and contact orientation. Given the qualitative nature of the study, participants’ own theories-in-use were important in the study. Also, Confucianism principles were significant in the Japanese assessment of teacher effectiveness. The study has implications for professors across fields since the majority of professors are educators who have not been formally trained in the education field. The study points to the importance of ongoing faculty development in teacher effectiveness.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".