Open Educational Practices (OEPs) for Research Skill Development with In-Service School Teachers
Bibliographic record
Abstract
Abstract In this chapter, we discuss open educational practices (OEPs) that Teacher Educators (TEs) used to facilitate in-service schoolteachers’ (I-STs’) research thinking. The majority of graduate students in the program held teaching roles in K-12 or educational development and training roles in adult learning contexts. OEPs are participatory and collaborative learning opportunities based on social constructivist principles used in a component of a fully online Master’s program in Education offered by a research university situated on the Canadian prairie. The I-STs in the program were situated as scholars of the profession and were provided with structured learning opportunities to help develop research-based skills (Brown et al. in Open Educational Practices (OEP) create conditions for learning in a graduate school, 2022; Jacobsen et al. in J Univ Teach Learn Pract 15(4):1–18, 2018). Results from our study indicate that responsive teaching is integral to OEP and can help I-STs develop research skills and research thinking.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".