Science, technology, engineering, & mathematics, curricular integration, and the story form
Bibliographic record
Abstract
Abstract As science, technology, engineering, and mathematics (STEM) education continues to increase in popularity, it becomes imperative that generalist preservice teachers (PT) have both strong concept knowledge and pedagogical skills to properly support its integration. However, generalist PTs do not have enough knowledge or skills possessed by those in STEM's respective disciplines, impacting their perceptions of how the framework is disseminated. The finger, then, is pointed at PT education to provide the necessary education and training that would allow for high‐quality STEM education beginning at the elementary level. One novel approach to mitigate this problem is to introduce Kieran Egan's education theory on imagination (mythic understanding) and the theory of integrated curricula to PT. Throughout this philosophical inquiry, we explore integrated curriculum models, imagination (mythic understanding) and storytelling, illustrating how they may appear in a STEM‐oriented lesson within an elementary science PT course, and attend to the need for approachable, evidence‐based interventions regarding generalist PT STEM 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".