Developing a Higher Order Thinking Skills Module for Weak ESL Learners
Bibliographic record
Abstract
The problem of mastering English does not involve students alone. The English language teachers, policy makers and curriculum developers are also affected. Thus, teachers have resorted to using higher order thinking skills (HOT) as a means to teach writing to weak ESL learners. The study aimed at developing and validating a higher order thinking skills module for teaching writing to weak ESL learners. It employed a qualitative research paradigm using documents analysis, interviews, observations and validation form. It was conducted in two phases. The first phase was completed with needs analysis specifically identifying problems teachers faced in teaching writing using higher order thinking skills in six selected secondary schools. The ADDIE model approach was used by the ESL teachers and experts in HOTs to create the module content. In the second phase, the teachers were observed ten times to investigate the effectiveness of using the HOTs module developed for teaching writing. The findings revealed that the HOTs module served as a guideline for the teachers in applying and integrating thinking skills in the process of teaching writing. These findings were used to guide decisions on implementing the appropriate teaching pedagogy to apply HOTS for teaching writing.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".