Understanding Science Teachers’ Implementations of Integrated STEM: Teacher Perceptions and Practice
Bibliographic record
Abstract
This study examines how science teachers experience integrating science, technology, engineering, and mathematics (STEM) approaches into their teaching. In addition, it further examines the encountered challenges in this regard to shed light on STEM current practices within the context of United Arab Emirates (UAE). This study consists of two stages; the first involved collecting qualitative data using semi-structured interviews to explore three science teachers’ perceptions and lived experiences having infused STEM into their regular teaching in cycle 2 for more than two years. Quantitative data were collected and analyzed in the second phase via the developed closed-ended questionnaire to examine teachers’ perceptions across a larger sample regarding “challenges encountered by teachers when implementing STEM teaching”. Research findings showed that science teachers generally have a positive attitude towards using STEM-based activities. In addition, data revealed that participants implement integrated STEM into their teaching frequently and regularly. Results also indicated teachers encounter challenges while implementing STEM: documentation, the vast curriculum content, and lack of time. Moreover, external challenges (i.e., the lack of supportive guidelines) rather than teachers’ competency (i.e., having sufficient knowledge and skills for implementing STEM teaching) appeared to have the highest impending impact. Finally, we discuss findings and presented implications for teachers, educators, and policymakers.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".