Communicating educational research to teachers through features of social media and modeling on a blogging platform
Bibliographic record
Abstract
In the field of education, there is a communication divide between researchers and practitioners (e.g., Vanderlinde et al., 2010; Anderman 2011; Cochran-Smith & Lytle, 1990). Researchers find it difficult to disseminate their results to practitioners efficiently (Chafouleas & Riley-Tilmad 2005; Huberman, 1993) and practitioners, among other difficulties, find research too complex to understand, synthesize, and apply in the classroom (Vanderlinde, 2010). With a blog that pairs educational theories with classroom activities, this study disseminated research to teachers in an understandable, applicable form. Participants were randomly assigned to one of three conditions that varied in levels of communication. The control condition was exposed to a static website with unilateral communication. The first experimental condition experienced a natural blog that allowed teachers to communicate with each other and the researchers, and the second experimental condition was exposed to a vicarious experience blog. This condition allowed for communication and included instances of implicit written modeling in the form of comments from confederate teachers (who were actually researchers) that detailed their experience with applying educational research in the classroom. Using a pre- and post-test assessments to evaluate teachers’ level of educational research content knowledge, results showed higher levels of learning in the groups with the implicit modeling compared to the control condition, however no difference in learning outcomes between the natural blog condition and control.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".