Examining the Utilisation of Extensive Reading from the Perspective of ESL Students’ Reader Response
Bibliographic record
Abstract
This qualitative experimental study examines the utilization of an Extensive Reading Programme from the perspective of ESL students' reader responses. It seeks to examine the kind of written responses produced by the mediocre and low proficiency ESL students based on the Reader Response Approach, the outcome of using Reader Response Approach in the Extensive Reading Programme, and the ESL students' experiences towards the Extensive Reading Programme. The study was conducted for a duration of three months in a secondary school. The participants of this study included an ESL teacher and six Form 4 students, comprising three females and three males. The researcher documented data through four sources: classroom observations, interviews with the teacher and students, response journals, and examination marks. Findings indicated that the Extensive Reading Programme and the use of the Reader Response Approach enabled the students to come up with various statements and benefited their language development. The findings of the study suggest that the Extensive Reading Programme and Reader Response Approach should be a part of the curriculum in secondary schools as they help in improving students' language proficiency. The study proposes a guideline for implementing an Extensive Reading Programme and Reader Response Approach in the language classroom, which ESL educators can adopt.
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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 | 0.010 | 0.023 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".