Establishing Communities of Practice for Effective and Sustainable Professional Development for Blended Learning
Bibliographic record
Abstract
The growing need for professional development to help university instructors with the adoption of online teaching is being propelled from several directions. But innovative professional development initiatives, intended to help university instructors better leverage technology, particularly through blended approaches, are not without tensions. The objective of this research study was to delve into these tensions. Directors in several North American professional development centres were interviewed in order to explore how their programs supported the integration of technology into teaching. Findings suggest that there is a tension between what professional development centres are doing and what they would like to do regarding: (1) deeper integration of technology into their own teaching practices as a centre, including blended approaches; and (2) how to nurture communities of practice, comprised of university instructors focused on teaching-related issues in higher education, such as adoption of blended learning strategies. Four themes emerged: uncertainty about how best to leverage technology and blended learning, questions regarding a professional development centre’s role in cultivating communities, the importance of being strategic, and desire for scalability. The chapter concludes with policy implications and recommendations for future development of effective and sustainable professional development practices.Request access from your librarian to read this chapter's full text.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".