Reimagining the Mathematics Curriculum Through a Cross-Curricular and Maker Education Lens
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".