Mathematics Teachers' Perceptions the Steam Approach: Science, Technology, Engineering, Arts, and Mathematics and Its Relationship with Some Variables
Bibliographic record
Abstract
This paper aimed to investigate the perceptions of mathematics teachers in Saudi Arabia regarding the STEAM (Science, Technology, Engineering, Arts, and Mathematics) approach. A sample of 350 teachers from the Eastern Province completed a 40-item survey on their STEAM perceptions and teaching requirements. Descriptive and inferential statistical analyses revealed overall positive perspectives, with 78.6% strongly agreeing that STEAM transforms classrooms into creative environments. However, just 58.4% felt it enabled active learning, and 67.4% were unsure about systemic support. Significant differences emerged based on teacher gender and qualifications, but not experience levels or stages. While largely optimistic attitudes exist toward STEAM's value, persistent resourcing, competency, and policy barriers likely impede classroom adoption. Recommendations encompass boosting investments in STEAM infrastructure, aligned teacher professional development, specialized materials and tools, and integration support across subjects. Further research incorporating mixed methods, expanded samples, and longitudinal tracking can delineate evidence-based strategies to catalyze effective STEAM adoption. The study recommended enhancing the effectiveness of the STEAM approach in education, especially in mathematics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".