Unveiling Teachers’ Instructional Self-efficacy in Science, Mathematics, and Technology: Personal and Contextual Influences
Bibliographic record
Abstract
Abstract Teachers’ self-efficacy, their ability to efficiently handle the tasks, responsibilities, and challenges related to their occupation, is a key determinant of students’ achievement and motivation in STEM subjects. Thus, this study seeks to investigate teachers’ instructional self-efficacy in science, mathematics, and technology in Qatar, using Bandura’s self-efficacy theory as a theoretical framework. A quantitative exploratory research methodology was adopted, involving 322 middle and high school teachers from both public and private schools. Data analysis included factor analysis, reliability tests, descriptive statistics, and non-parametric tests. Results indicate that, despite teachers’ high self-efficacy in STEM instruction, significant differences were observed across gender, school type, educational level taught, and academic degree achieved. The results further demonstrated significantly higher self-efficacy among male teachers ( p < 0.05), high school teachers ( p < 0.05), private school teachers ( p < 0.05), and teachers with higher educational degrees ( p < 0.001), compared to their counterparts. This study recommends enhancing teachers’ positive attitudes towards STEM-inclusive education through increased professional development opportunities. These findings provide valuable insights for educational stakeholders seeking to improve teacher performance and well-being in Qatar’s schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".