Reimagining the Mathematics Curriculum Through a Cross-Curricular and Maker Education Lens
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
Despite the positive impact of maker education on student learning, challenges towards its implementation in formal school settings still exist. There is limited research on maker education in teacher education programs and a lack of knowledge on how to integrate it into the mathematics classroom. To address these issues, the following research questions were examined: What is the nature of the productive design features of maker education for teacher candidates? What are the benefits and challenges of these opportunities for teacher candidates learning to teach mathematics? The methods used were a case study interlinked with design-based research. A total of 114 teacher candidates participated in the study. The research findings have implications for educators who design/implement maker education curricula into STEM courses. For educators and researchers, the maker education opportunities from this study contribute to further re-imagining learning competencies, pedagogy, and resources in teaching mathematics and other STEM disciplines.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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