Exploring Reading Engagement in English as an International Language (EIL) Materials
Bibliographic record
Abstract
The popularity of English as an International Language has been rapidly growing, contributing EIL materials to have become more widespread in learning. This study discusses students’ reading engagement and their perception of EIL materials, which aims to (1) investigate students’ engagement in reading EIL materials, (2) explore student perception of EIL materials, and (3) explore the relationship between students’ level of reading engagement and their perception of EIL materials. 113 undergraduate students at one private college who use English as a medium for their instruction were participated in this study. A mixed-method study which Reading Engagement Questionnaire and EIL Perception Questionnaire were adopted and constructed to explore the findings and an interview protocol was designed to triangulate with the questionnaires’ findings. The study revealed that students engaged in reading EIL materials in a neutral level and perceived EIL materials positively. Students possessed all four constructs of reading engagement in EIL materials: competence-related beliefs, task values, behavioral engagement in reading texts, and engaged readers. Furthermore, students perceived positively with four principles of EIL: current status of English, varieties of English, strategies for multilingual/multicultural communication and English reader’s identities. Interestingly, there was a positive relationship between students’ reading engagement and their perception of EIL materials.
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".