Mathematical Modeling: Issues and Challenges in Mathematics Education and Teaching
Bibliographic record
Abstract
Mathematics education researchers and policy documents in the United States have expressed the need to improve the teaching and learning of mathematical modeling at the K–12 levels so that students can apply their knowledge of mathematics to solve real-world situations. Unfortunately, most practicing teachers (PTs) and preservice teachers (PSTs) acquire didactical and pedagogical styles that do not support effective modeling practices. To investigate these dilemmas, this study examined PTs’ pedagogical experiences in and PSTs’ perspectives on mathematical modeling practices. Participants included 62 PTs and 18 PSTs from a Midwestern region of the United States. Data originated from questionnaire items and open-ended questions, which were analyzed quantitatively and qualitatively. Varied participants’ ideas on mathematical modeling practices were identified, recorded, and summarized. Results indicated that most of these PTs and PSTs have little to no experiences with mathematical modeling practices and associated pedagogies. Such results along with a supplemental discussion have implications for teacher education programs and professional development centered on mathematical modeling education.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".