An Integrated Model to Implement Contextual Learning with Virtual Learning Environment for Promoting Higher Order Thinking Skills in Malaysian Secondary Schools
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the important features in developing science curriculum in Malaysia is the emphasis on the education system that allows students to master higher order thinking skills (HOTS). However, the current teaching practice tends to inhibit students’ HOTS by which students are given drills and tutorials to perform better in examination. The contextual learning is deem as a suitable approach to develop HOTS, as it enables students to build their knowledge in the context of their minds, then later makes use of linkages and applies it to their real life. Additionally, the advancement of technology offers opportunity for integrated contextual learning with Virtual Learning Environment (VLE) to foster HOTS. This study aims at developing an integrated model to implement contextual learning with VLE for promoting HOTS in Malaysian schools. Using the constant comparison analysis for analyzing the literature, this study analyzed previous literatures to develop the integrated model. Findings show that limited research exists on the integration of contextual learning with VLE to promote HOTS, leaving some vacuum for further study and improvement and the need to formulate an integrated model.
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.
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 | 0.003 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it