Online Professional Development: Three Approaches for Engaging Faculty through a Constructivist Framework
Bibliographic record
Abstract
At a time when educational technology is in constant flux, some consider the professional development of teachers to be at the foundation of change (Dede, Jass Ketelhut, Whitehouse, Breit & McCloskey, 2009). Particularly with the introduction of technology into learning contexts, there exists opportunity for professional development (PD) reform in which faculty experience the same Web 2.0 technologies and social media connections as their students. Exploration of PD with such technologies presents possibilities for their use in educational settings, while also engaging faculty in 21st century learning. Having teachers explore these skills in a meaningful application context, their knowledge is permitted to evolve and change with each activity (Driscoll, 2005). This paper explores the opportunities and challenges of Web 2.0 technologies through three online learning designs: direct instruction, professional learning communities and online mentoring by way of a constructivist lens.
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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 | 0.054 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".