Optimizing EFL Learners’ Sensitizing Reading Skill: Development of Local Content-Based Textbook
Bibliographic record
Abstract
The development of local wisdom based sensitizing reading material is aimed at penetrating one of the imperishable gaps between authentic and non-authentic reading materials dispute in an EFL teaching context. Promoting EFL learners’ needs for the first semester students of English department at university level, who rarely or even never have a direct contact with native speakers, with meaningful and contextual reading textbook in an EFL setting is worth contributing. This study utilizes research and development paradigm within four stages, namely planning, development, try-out, and textbook revision. The textbook development resulted fifteen chapters containing fifteen local reading passages from various famous local tourism objects, famous public figures, cultures, traditional cuisines, and music. Each chapter encompasses eight exercises generated from the reading text itself with approximately from 400 to 600 words. Those exercises cover picture reading preview and identification, lexical sets, collocation and formation, literal, interpretive, and critical comprehension, and networking activity through interview and writing activity beyond the text. The average result of textbook validation from reading experts, English practitioners, and learners revealed the average score was 3.76 within the interval 1 to 4 and it was sorted out into ‘good’ category. Further, revisions toward grammatical error, diction, instruction clarity and picture lay out were also addressed and refined. As the textbook effectiveness toward the improvement of learners’ reading comprehension is not measured yet, so an experimental study is welcome for further research to address the issue of textbook effectiveness.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".