Methods and Strategies for STEM Inquiry: Science and Mathematics Integration (SAMI) Project
Bibliographic record
Abstract
Although STEM education has been promoted by scientists and educators, the disciplines of Science and Technology, and Mathematics are taught separately in school, and teachers are not given the opportunity to integrate the disciplines into a cohesive learning paradigm based on real-world applications. Integrating Mathematics into science lessons using hands-on and technology design activities is critical to strengthening students’ development of authentic technological problem-solving skills. The Science and Math Integration (SAMI) Project engages teacher candidates in developing culminating project lesson plans aimed at using mathematics as a tool to create a concrete product (e.g. a bridge) or design an experiment or an investigation where they use Math skills (e.g. measure the amount of reactants and rates of reaction). During the last three years, the SAMI project was assigned to about 200 teacher candidates as one of their culminating projects. Project evaluations show that the students realised the practical applications of mathematics in science, and the project helped them to understand the role of mathematics in technological designs and in our daily lives. What we have learned from SAMI projects is that, teaching STEM subjects in isolation will not offer the same authentic nature of project-based learning that integration does.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".