WHY IS THERE A LACK OF INTEREST? A DECLINE IN STEM SUBJECT SELECTION AMONG STUDENTS FROM THE TEACHERS’ PERSPECTIVE
Bibliographic record
Abstract
Malaysia has yet to achieve the national STEM Policy target ratio of 60:40, which aspires for 60% of students to pursue science and technical streams, and 40% in the arts/social science streams. This study was conducted to identify three main objectives: 1) the factors contributing to the decline in STEM subject selection, 2) the challenges faced in the teaching and learning (PdPc) of STEM subjects, and 3) the most appropriate methods to address the declining interest in STEM subjects. A qualitative approach was employed through interviews with five secondary school teachers in the state of Perlis. The findings revealed three key themes regarding the decline in STEM subject selection: the perception that science streams are difficult, the complexity of the syllabus, and students' lack of interest. Challenges in STEM teaching and learning were categorized into four themes: students’ weak foundation in STEM, teachers’ limited proficiency in using technology, limited funding for experiments and projects, and a lack of awareness about STEM career opportunities. Recommendations to improve STEM subject selection yielded four themes: the need for parental support in encouraging students to choose STEM subjects, targeted training for teachers to master the STEM syllabus, stronger support from relevant authorities, and collaboration with higher education institutions or local agencies to boost students’ interest in pursuing the STEM stream. This study contributes by offering guideline references for stakeholders to achieve the goals set by the National STEM Policy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".