English For Science and Technology (EST): Learning and Teaching Strategies for High School Students in Malaysia
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
Programme for International Students Assessment (PISA) 2015 which highlighted Global Rating on Education Quality reported that Malaysia was ranked in the 52nd placing out of 72 countries in assessing students’ ability in Science and Mathematics using English language (OED, 2016). This implies that Malaysian students need to improve further on science and technology development. Therefore, this study looked into the learning strategies and problems students face in learning English for Science and Technology (EST) subject and the teaching strategies employed by the teachers. A total of 150 Form Four students from three selected schools were taken as samples for this study. Data was collected using questionnaires. The researchers conducted a structured interview with 27 students from three schools (in Kedah, Selangor and Negeri Sembilan). In order to triangulate the data, the researchers also carried out a classroom observation on the Form Four classes from each of the three states and analysed 30 sample scripts of EST report writing to look at the types of language problems made by students. The study applied a mixed method of quantitative and qualitative. Findings from the 150 students’ Likert-scale questionnaires were analysed using a One-Sample T-Test. The responses from the 27 students’ interviews were transcribed and analysed using content analysis. The observations carried out in the selected Form Four classes were transcribed in a narrative written report. Overall, the study depicts that the majority of the students face language problems in learning EST. Students were found to be lacking in applying appropriate learning strategies, and teachers still control EST classrooms. Through the findings, this study suggests that a comprehensive and effective learning and teaching strategy, also known as CALLA (Cognitive and Academic Language Learning Approach), will help students elevate their comprehension and application to understand scientific knowledge in a second language effectively.
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 | 0.011 | 0.256 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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