Enhancing Pre-Service Teachers’ Integration of STEM Education into Home Economics Lessons Through A Professional Development Program
Bibliographic record
Abstract
This research was aimed to assess whether a newly developed professional development (PD) program enhances STEM-based teaching practices among pre-service home economics teachers. The activities in this PD program were divided into three parts: knowledge about STEM education, lesson plan development, and implementation of STEM-based lessons. Using three pre-service home economics teachers as case studies, data were collected throughout the PD program from group discussions, observations, interviews, and review of documentation. Data were analyzed using content analysis. The findings demonstrated that the pre-service teachers gained more confidence with integrating STEM education into their lesson plans as a result of the PD program. In addition, they were able to link content about home economics to other disciplines. This integration provided more opportunities for students to test their own ideas, ask questions, and apply 21st century skills. STEM knowledge, school context, students’ learning style, and time constraints were identified as the main factors that impacted their teaching practices. Results from this study provides insight on how to better prepare teachers outside of the STEM disciplines with integrating STEM content into their teaching practices and provides a framework for future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".