Evidence-Based Research in STEM Teacher Education: From Theory to Practice
Bibliographic record
Abstract
The paper identifies a possible cause of previous STEM education reform failures and suggests how repairing the link between evidence-based education research and teacher education practice may be a potential solution. The evidence-based STEM education research is described and placed in the general education research context to illustrate how research-based and STEM-focused teacher education can address some of the biggest challenges facing contemporary educators: the growing student disengagement, the paucity of successful active learning environments, the inadequate attention to educating and supporting teachers, the scarcity of evidence-based research on student STEM learning that can inform both teacher education practice and policy. The paper calls on placing research-based STEM teacher education in the centre of contemporary reform efforts and conducting evidence-based education research to study the effects of this process on the growth of teacher knowledge and consequently on student STEM learning. Specifically, using research-based evidence for the development of teachers’ knowledge for STEM teaching and their positive attitudes about learning (the growth mindset) are identified as possible key factors in successful STEM education reform efforts. However, more research needs to be done to examine this assertion. To do so, we suggest a four-step approach for incorporating evidence-based STEM education research into teacher education practice: Model-Reflect-Research-Practice. This approach emphasizes teacher-candidates’ active engagement with research-based pedagogies as students and as future teachers. It provides a structure for incorporating research-based pedagogies in STEM teacher education as described in the examples. The first example showcases Peer Instruction supported by PeerWise technology to engage teacher-candidates in designing STEM learning environments that promote active learning and conceptual understanding through peer learning. The second example focuses on supporting teacher-candidates’ growth by asking them to teach short mini-lessons, record and upload them onto the online collaborative platform (Collaborative Learning Annotation System) for peer feedback and reflection. Both examples incorporate collaborative educational technologies to promote the development of teacher-candidates’ knowledge and their growth mindset. The paper emphasizes how making evidence-based STEM education research a foundation of teacher education can help connect education research to teaching practice and break the vicious circle of STEM education reform failures.
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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 | 0.333 | 0.512 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.034 | 0.041 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.019 | 0.029 |
| 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".