The Reality of Malaysian ESL Teachers’ ICT Pedagogical Practices: Challenges and Suggestions
Bibliographic record
Abstract
Teachers of English as a second language (ESL) are nowadays exploring the integration of Information and Communications Technology (ICT) tools into their Higher Order Thinking (HOT) pedagogical practices. However, there are various challenges in using ICT to teach Higher Order Thinking Skills (HOTS), as it is only explored superficially. This study investigates the challenges encountered by ESL teachers when using ICT to promote HOTS. Meanwhile it aims to provide a set of guidelines to help teachers in teaching HOTS through utilising ICT. The framework of the research is grounded on Bloom’s Revised Taxonomy (2011). 30 ESL teachers from 5 schools selected by the ESL Master Teachers’ affiliation, participated in answering to the questionnaires. Meanwhile, 5 ESL teachers from each school were involved in focus group discussions (which were used to triangulate the data obtained from the questionnaires. The FGDs lasted for 20 minutes per session, and they were conducted based on the availability of the ESL teachers. The collected data were analysed in the form of tables and direct excerpts. The findings support that ESL teachers face multiple challenges such as time constraint, poor internet connection, and lack of ICT tools while using ICT to promote HOTS in schools. The findings of the present study are in-line with Malaysia’s vision in shifting the focus of education into the acquisition of HOTS. ESL teachers benefit from this research as they can improve on their teaching pedagogies based on the suggestions presented in this study.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".