Full STEAM Ahead: Building Preservice Teachers’ Capacity in Makerspace Pedagogies
Bibliographic record
Abstract
This paper explores teacher candidates’ understandings of 1) makerspace/constructionist pedagogies; 2) the issue of bullying; and, 3) working with at-risk youth, as they evolved over the course of a six-month partnership. The partnership included researchers and teacher candidates at a Faculty of Education and the teacher librarian at a local elementary school who were participating in a larger Social Sciences and Humanities Research Council of Canada (SSHRC)- funded project that focuses on building, implementing and evaluating an effective model for a school improvement program that increases teachers’ capacity, experience and specific fluency and expertise with technologies supporting STEAM learning and digital literacies. In this paper, we discuss qualitative ethnographic case study research, which examines in depth the experiences of five teacher candidates as they worked with 20 students in a grade 6 class in a high needs school on makerspace activities related to bullying prevention in their school community. Qualitative research documentation includes digital video and audio recordings, on the-ground field notes and observational notes, pre and post interviews with participants and focus group sessions. Results from this study contribute new knowledge in the areas of preservice teacher development and digitally-enhanced learning environments for K-6 learners.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".