Assessing Teachers Education and Professional Development needs to Implement STEM after Participating in an Intensive Summer Professional Development Program: Teacher professtional development and STEM
Bibliographic record
Abstract
Studies suggest several key aspects of STEM (science, technology, engineering, mathematics) integration for teachers, but translating the findings and recommendations of these studies into fruitful changes in teachers’ classroom practices remains a challenge. In this study, an assessment of teachers participated in an intensive professional development STEM program was conducted to better understand their perspectives on the content of the program, their anticipated challenges to effectively implement STEM education in their schools, and the supports needed to help them overcome their challenges. Both quantitative (surveys) and qualitative (participant interviews) were used to collect data to examine the impact of program on teachers’ content knowledge, their anticipated challenges, and the supports needed to integrate STEM in their classroom. Results showed that the majority of the participants reported that the program enhanced their knowledge and abilities on how to teach science through STEM approach. Participants also reported several anticipated challenges that will limit their integration of STEM in the classroom such as; lack of physical resources, dealing with students’ expectations, attitudes, and abilities, lack of time for collaboration with other teachers, and other important administrative challenges. Participants also provided specific suggestions to support their integration of STEM education in their classrooms.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".