Knowledge, Efficacy, and Experience in Components of STEM Education: The Impact of Teacher Professional Development
Bibliographic record
Abstract
Abstract Around the globe, there has been a significant focus on ensuring youth receive sufficient education and training in Science, Technology, Engineering, and Math (STEM) domains. Education reforms often begin with educators, who are responsible for effectively implementing curriculum changes. However, in Canada, although STEM education reform is underway, teacher education and professional learning (PL) and development may not have kept pace, potentially leaving some teachers feeling unprepared to integrate these changes confidently and effectively. This dissertation introduces two studies that investigate pre-service teachers’ readiness to teach STEM domains in the classroom, as well as the impact of a PL intervention aimed at improving teacher knowledge and skills in components of STEM. In both studies, teacher readiness was evaluated through the lens of self-efficacy theory (Bandura 1977, 1986) and the Technological Pedagogical Content Knowledge (TPACK; Mishra & Koehler, 2006) framework. In Study 1, pre-service teachers from Canadian Bachelor of Education programs (n = 216) completed a comprehensive survey to establish a baseline for their experiences, knowledge, and perceptions, and to investigate and understand factors that impact pre-service teachers’ preparedness to teach STEM education. Overall, outcomes indicated that pre-service teachers are moderately prepared to teach some components of STEM, but they may benefit from additional PL opportunities to increase confidence and knowledge. These resources may be especially helpful for individuals who lack an educational background or interest in science. In Study 2, pre-service teachers (n = 47) were offered an evidence-based, long-term, online PL focused on teaching STEM using cross-curricular approaches. Pre- and post-test measures focused on teaching efficacy, outcome expectations, TPACK for teaching science, STEM PL need, and confidence with course concepts. Overall, results suggest that the PL was effective at increasing pre-service teachers teaching self-efficacy in science, TPACK, confidence with course concepts, confidence teaching STEM using an integrated approach and decreasing the need for science and technology pedagogy PL.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".