Re-Making Teacher Professional Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it