A Comprehensive Review of the Components for Process Approach to Overcome Writing Impairments among Low Proficiency Learners in Malaysia
Bibliographic record
Abstract
Researchers and educators have both expressed interest in the English as a Second Language (ESL) ability of Malaysian students. Due to the Malaysia's multilingualism and the significance of English in international communication, learning outcomes must be improved through the use of efficient pedagogical approaches. Accordingly, this systematic literature review (SLR) examines process approach in writing and its application specifically focusing on how it affects low-proficiency and ESL learners in Malaysia when it comes to communication achievement. By using a qualitative research approach this study has examined published literature from 2019 to 2023 across three databases: Scopus, Web of Sciences, and Google Scholar. 16 publications were extracted out of 763 related articles based on the inclusion and exclusion criteria. The findings revealed that the Process Approach has the potential to improve ESL students' writing abilities, but putting it into practice calls for a careful grasp of the regional context, particularly in Malaysia. The advantages of this strategy can be maximized by addressing issues like time restrictions and pedagogical considerations, as well as by using complementing components like planning, brainstorming, and mind-mapping. Future research is suggested to fill the gap in this study by investigating other relevant approaches to writing effectively and efficiently for students.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".