Exploring The Need of Teaching Module for Enhancing Higher-Order Thinking Skills
Bibliographic record
Abstract
The purpose of this research is to analyze the necessity for the development of a scientific teaching module based on students' HOTS. Empowerment of HOTS in primary school Science Education is a special requirement to ensure students achieve the 6 student aspirations outlined in the Malaysian Education Development Plan. Specifically, this study explores the problems and needs to develop a teaching module in science subjects that apply Higher Order Thinking Skills (HOTS). The development of this module is based on the ADDIE model which consists of analysis, design, development, evaluation, and implementation phases. This study uses a semi-structured interview method on six Science teachers of the Ministry of Education (MOE) from different schools. The objective of the analysis phase was to identify the need to develop teaching modules for electrical topics under the Science syllabus. To find out the need for the development of the module, an interview was conducted with six science teachers who are experts in the field in the Seremban area. Four themes emerged from the need analysis namely; (1) The importance of learning electrical topics, (2) Problems in the teaching and learning of Science, (3) Teaching strategies, and (4) Desired improvements. The development of this module contributes to teaching and facilitation to improve students' HOTS in primary school science subjects.
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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 | 0.014 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".