How has teaching effectiveness been conceptualized? Questioning the consistency between definition and measure
Bibliographic record
Abstract
Teaching effectiveness (TE) research impacts educators and their careers, learners and the quality of education they receive, and administrators and the organizations they safeguard. However, there is a lack of consistency in how TE has been conceptualized: many papers used inaccurate or implied definitions of TE, or despite discussing and often measuring TE, did not define TE—other papers defined TE without proposed measures or provided measures of the concept without defining it. We found two dimensions of TE, student-focused (outcome) and educator-focused (input), and an existing TE definition evaluated as the strongest for both dimensions. Further, TE measurements may be summarized in five categories: student evaluation of teaching effectiveness (SETE), objective measures, peer review, administrative evaluation, and self-reflection. To conceptualize TE, our findings suggest pairing the TE student-focused construct with SETE and objective measures, while the educator-focused dimensions of TE should be measured with peer review, self-assessment, and administrator evaluation. By consistently conceptualizing TE, researchers may contribute to rigorous research and work together to consistently add to the body of knowledge, thus furthering the quality of TE research.
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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 | 0.211 | 0.420 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.029 | 0.029 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| 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".