Unleashing the Learners: Teacher Self-Efficacy in Facilitating School-Based Makerspaces
Bibliographic record
Abstract
This qualitative research project explored the key characteristics, attitudes, and experiences of makerspace facilitators in Saskatchewan. The aim was to gather knowledge and wisdom from early adopters of makerspace from a variety of contexts ranging from tinkerspaces to increasingly popular school-based spaces in order to inform early and career-educators of the skills and attitudes conducive to creating and leading dynamic activity spaces. The questions for the semi-structured interviews were based on Bandura’s (1977; 1997) self-efficacy expectations: performance accomplishments, vicarious experience, verbal persuasion, and emotional arousal. The findings align with those of other studies in that they point towards key areas of experience: the value of productive failure, relinquishing control, and modes of support. We conclude that there is a need to help preservice and early career educators to become prepared and confident makerspace facilitators. To this end, we offer four suggestions for new makerspace facilitators: aim towards unleashing, allow others to be the experts and leaders, celebrate success and failure, and openly seek and offer support. Keywords: makerspace, self-efficacy, motivation, early career educators, productive failure
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".