Perceptions and Approaches to Teaching of Award-Winning Teachers at Research Intensive Universities Internationally
Bibliographic record
Abstract
This chapter provides an overview of a research project conducted with award-winning teachers at research-intensive universities to investigate a number of areas including approaches to teaching and learning and the use of technology in teaching, as well as views on staff development to enhance teaching quality and the recognition of teaching in promotion and tenure. In an analysis of data, the predominant approach taken by the award-winning teachers in this study was an approach that has been described as a conceptual change/student-focused approach to teaching versus an information transmission/teacher-focused approach. The former approach is consistent with students taking a deeper approach to learning. It was also found that the use of technology in teaching in this group of award-winning teachers extended beyond content delivery to provide opportunities for active pre, post, and in class learning. Examples of how technology affordances were used is provided. In addition, their views and suggestions on staff development programs and the research/teaching nexus are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".