Making and Active Learning in Higher Education
Bibliographic record
Abstract
In this study, the authors researched, designed, and implemented maker education opportunities into teacher candidate training, specifically in elementary mathematics and science methods courses. To investigate the impacts of active learning and maker education models in the teacher education program, the researchers observed, interacted with, and asked teacher candidates (1) which instructional design practices were helpful, (2) what they learned (i.e., knowledge gained, effective pedagogies, and teaching methods) and (3) what were the impacts of these learning opportunities in the context of learning to teach mathematics and other STEM subjects? The Maker Ed workshops involved creating opportunities for teacher candidates to gain experience of how to make, exploring ways to incorporate making in a variety of contexts and then extending this learning to their own pedagogy. To better prepare students for the workforce and everyday living, life skills, transferable skills, and workforce competencies need to be taught through student-centered and activating instructional practices.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".