Exploring Challenges of Dyslexic Students Learning English as a Second Language: Reading Drama Script
Bibliographic record
Abstract
This study aimed to explore the reading challenges faced by dyslexic students learning English as a second language and interventions to support their reading skills. A mixed research design (both qualitative and quantitative) was used. Data was collected through a questionnaire, semi-structured interview and observation. A judgmental sampling technique was employed to select dyslexic students and teachers at two public schools in Tiruvallur district. Twenty-six students with English dyslexia and two teachers were selected for this study. Data were collected through the use of schedule questionnaire, interview and classroom observation. Statistical package for social Science Software version 26 was employed to analyze quantitative data, whereas the thematic analysis method was used for interview and classroom observation data analysis. The findings of this study showed that students encounter difficulties in phonological awareness, decoding words, recognizing patterns and comprehension. Besides, the study revealed that employing a multisensory and structured approach to teaching, incorporating visual and auditory, are crucial strategies for enhancing students English reading skills. The research also explored that explicit reading instruction and assistive technology significantly improve students’ reading drama script skills. The findings also identified that early identification, targeted instruction and individualized support for dyslexic students are significant elements in progressing their English reading skills. However, the study also acknowledges the limitations and suggests future researchers investigate the impact of parental involvement on dyslexic students’ English reading drama script skills.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".