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 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".