Assimilation of the KWHL Model: A Review of Learning and Facilitation (LaF) of HOTS for Argumentative Essay Writing
Bibliographic record
Abstract
It is a necessity for teachers to develop higher-order thinking skills (HOTS) into learning and facilitation (hereafter known as LaF) processes implemented in the classroom. Teachers should carefully balance the content of knowledge or knowledge they wish to convey and then integrate with other skills, especially HOTS. This research set out to examine the assimilation of HOTS into the LaF of argumentative essay writing as carried out by Malay language teachers in secondary schools. To obtain a holistic overview of the methods used by teachers in LaF, a qualitative case study approach was employed as the research design of this study. Two research participants were involved voluntarily in this study, and it was conducted at a boarding school in a district in Selangor, Malaysia. Data were collected through in-depth interviews and classroom observations with the two participants. The findings revealed that the assimilation of the KWHL model for the LaF of HOTS argumentative essay writing could be seen through (i) the pair-think-share activity; and (ii) usage of self-assessment whiteboard.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".