Harnessing Technology: Enhancing Literacy Development for ESL Secondary School Students
Bibliographic record
Abstract
This paper examines the innovative integration of technology in literacy development for English as a Second Language (ESL) learners, focusing specifically on secondary school students in Malaysia. As educational practices shift from traditional methods of the 20th century to advanced technologies such as Artificial Intelligence (AI), ESL students encounter unprecedented opportunities to engage more effectively with the English language. Employing a comprehensive literature review and thematic analysis methodology, this study identifies emerging themes in technology integration within ESL education. Key themes explored include the effectiveness of gamification, AI-assisted tools, and multimodal literacy, as well as the challenges posed by the digital divide, particularly concerning disparities in resource access between urban and rural areas. The findings highlight the urgent need for equitable access to technology and tailored interventions to support literacy development among all ESL learners. By prioritizing enhancements in technological infrastructure and fostering personalized learning experiences, educators and policymakers can bridge the literacy development gap. This paper advocates for a balanced approach to technology integration that promotes critical thinking, collaboration, and human interaction, ensuring that every student has the opportunity to fully leverage technology in their literacy development journey.
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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 | 0.004 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".