Multi-Faceted Professional Development Models Designed to Enhance Teaching and Learning within Universities
Bibliographic record
Abstract
In this chapter we advocate the reconceptualisation of pedagogical focused professional development to a more flexible and systematic approach and present two technology-oriented models. This chapter is of interest to a range of educational stakeholders including university professional developers, academics, leaders, students, and support staff. Two mixed method case studies of students' and academics' experiences of online and blended teaching and learning informed the design of the models. These multi-faceted models are designed to promote effective pedagogically-focused professional development, the scholarship of teaching and learning, social and professional networking, and supportive university leadership all aimed at improving teaching and learning. We articulate how the integration of technology can facilitate all of these important activities. It is anticipated that, if implemented, these models will result in a more pedagogically- and techno- efficacious academy; more satisfied and successful graduates; more informed, involved, and trusted leaders; greater sustainability for programmes; and the enhancement of institutional reputation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".