Blending Professional Development for Rural Educators An Exploratory Study
Bibliographic record
Abstract
Blended learning is a process by which educators use varied web-, print-, and classroom-based techniques to present a specific set of skills to a group of adult learners. In this chapter, the author argues that Rossett, Douglis, and Frazee’s (2003) blended learning model is superior to others’ as it is based on adult-learning principles. In March, 2007, the researcher and one colleague conducted a Social Sciences and Humanities Research Council (SSHRC) federal study on teacher supply and demand issues in Northern Canada. As part of the questionnaire and interview data, the participants (n = 113) were asked to comment on professional development models currently used and models to be considered. In particular, comments on the use of blended learning as a viable method of e-professional development model were favourable. In follow up to those comments, the main researcher provided professional development model exemplars and asked the participants to discuss the advantages and disadvantages for the rural professionals. The results of this study are promising as the majority of participants chose blended learning as their primary choice for professional development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".