LEVEL OF PREPARATION AND TEACHER'S TRAINING REQUIREMENTS IN IMPLEMENTING THE HOTS IN SCIENCE TEACHING
Bibliographic record
Abstract
The purpose of this study is to identify the extent of readiness of national elementary science teachers in the Kuala Pilah District to apply the High Order Thinking Skills (HOTS) teaching as well as the level of HOTS-focused training needs in Science teaching. Quantitative research using the survey method and questionnaire as a research instrument. The sampling method was used to select respondents consisting of 124 National School Science teachers in Kuala Pilah District, Negeri Sembilan. The data were analyzed using Winstep 3.71.0.1 software with Rasch Measurement Model approach for the description of descriptive data. The Cronbach Alpha value obtained in the pilot study was 0.95. The findings showed that Science teachers had a high level of knowledge in HOTS (mean score = 3.85, min size +0.29). This data indicates that this Science teacher has a high level of readiness to embrace HOTS basic knowledge, KBAT pedagogy knowledge, KBAT item building, and KBAT assessment to be applied in the classroom. The analysis also shows the level of teacher training requirement applied HOTS as a whole at a high level (mean score = 4.21, mean size = -0.87). This finding concludes that Science teachers have had a high level of readiness to implement KBAT in teaching Science, but they also require training to empower existing domains.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".