Creative approaches for 21st‐century Science, Technology, Engineering, and Mathematics teacher education: From theory to practice to policy
Bibliographic record
Abstract
From theory to practice to policyThis special issue addresses two crucial questions that teacher educators have been grappling with for nearly half a century: What qualities, attitudes, and skills should be nurtured in Science, Technology, Engineering, and Mathematics (STEM) teachers during their education (Ben-David Kolikant et al., 2020a, 2020b)?Additionally, how can we effectively connect research, practice, and policy in STEM teacher education to incorporate innovative 21st-century approaches?The authors contributing to this special issue aimed to explore creative strategies and programs for STEM teacher preparation and professional development, made possible by the advancements in educational technology, STEM education research, and our evolving understanding of student engagement in STEM learning.STEM education has long been a focal point of interest for governments, educators, and industry leaders (Kleinschmit et al., 2023).STEM disciplines equip students with essential 21st-century skills such as real-world problem-solving, knowledge construction, communication, and collaboration (Stehle & Peters-Burton, 2019).However, the constant and often rapid evolution of these fields presents significant challenges for integrating new developments into teacher education programs.This challenge is thoroughly examined by Zhan and Niu (2023), who trace the evolution of STEM education from its early stages.By analyzing keyword frequency in 903 K-12 papers from the Web of Science (WOS) database (published between 2009 and 2023), and 873 papers on higher education (published between 2004 and 2023), the authors highlight a gradual transformation and expansion in the scope of STEM education:In the early stages (2009 to 2014), the subjects of science, technology, engineering, and mathematics played a dominant role, and these subjects were considered to be the core of STEM education.Over time, science subjects such as computer science, arts, physics, and environmental science were gradually incorporated into the STEM education integration pathway.In the post-2019 period, more and more research has emerged in the humanities and social sciences.(Zhan & Niu, 2023, p. 2) This trajectory is clearly reflected in the 'Chongqing Plan' for STEAM teacher preparation (see Lin and Li, in this volume).The researchers then concluded Future work should prioritize the articulation of STEM subject integration between K-12 education and higher education.At the K-12 level, it is necessary to enhance vocational
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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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.035 | 0.026 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".