Designing effective STEM courses: a mixed-methods study of the impact of a STEM education course on teachers’ self-efficacy and course experiences
Bibliographic record
Abstract
Science, technology, engineering, and mathematics (STEM) fields play a critical role in the advancement of society and are expected to grow rapidly in the coming years. This study examines the development of a STEM education course and its impact on teachers’ self-efficacy and course experiences. The study involves a mixed-methods approach, using survey and course assignment results gathered from 52 mathematics master’s degree candidates who took an online STEM education course. Teachers’ self-efficacy, STEM knowledge, reflections from reading materials, and lesson plans were quantitatively analyzed while content analyses was employed for the teachers’ opinions on the role of each STEM subject and overall course evaluation. Results showed a significant increase in teachers’ self-efficacy in teaching STEM subjects after completing the course. Additionally, teachers reported positive experiences related to course content, activities, and assignments. The study provides insights into the design and implementation of effective STEM courses and provides practical implications for designing operative STEM courses.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".