Issues of ICT Usage among Malaysian Secondary School English Teachers
Bibliographic record
Abstract
This study explored on perception, usage and obstacles of using ICT in teaching English Language among secondary school English teachers. The advancement of technology has given a space for teachers to boost teaching and learning of English language in secondary schools. It is believed that integration of ICT could enhance quality of teaching and make learning very effective. This paper first examined on secondary school English teachers' perception about the integration of ICT in teaching English lessons. It is found that the majority of teachers have positive views about the integration of ICT in teaching English. Then, the study revealed about the usage level of ICT among secondary school English teachers. The findings showed that teachers are utilizing ICT equipments only for certain tasks such as finding for information and preparing PowerPoint presentations. They do not have much exposure about the other opportunities provided by ICT. Next, researchers identified the dominancy of obstacles faced by secondary school English teachers in ICT integration with English lessons. Finally, this study provides some implications of the study and recommendations which are useful for researchers to expand their study in this area. In addition, this study too provides information for teachers on the advantages of ICT. Therefore, teachers could make adjustment to their teaching methodology which may lead to effective ICT integrated teaching and learning of the English language.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".