Re-Making Teacher Professional Development
Bibliographic record
Abstract
With the introduction of the new two-year Bachelor of Education program across Ontario, our Faculty of Education has introduced a twenty-hour internship. This internship is meant to provide real-world teaching experience for teacher candidates, who are nearing the end of their formal education. By maker pedagogies, we refer to the inquiry-based, student-directed, constructionist approaches to learning typically used in makerspaces. Makerspaces have gained traction in Ontario classrooms, particularly in the last two years. These spaces and their pedagogies facilitate the development of students' global competencies (Hughes, 2017; Somanath et al., 2016). We welcomed eleven teacher candidates (TCs) into our STEAM 3D Maker Lab as part of their internship course for professional development (PD) to provide them with pedagogical experience in a makerspace environment. Our research focused on exploring how the TCs developed a better understanding of maker pedagogies and the associated tools through this PD. As the internship was created and facilitated by an education graduate student in the lab, we extended the research to also investigate this student's development in identifying and understanding some of the best practices associated with making as learning. Through analysis of the TCs' and graduate student's experiences, we identify some best practices in maker-focused professional development for beginning teachers.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".