Fostering Low English Proficiency Learners’ Reading in a Freshman EFL Reading Class: Effect of Using Electronic and Print Textbooks on Taiwanese University Students’ Reading Comprehension
Bibliographic record
Abstract
This study investigated differences in university students’ academic reading comprehension performance, reading strategy use, and perception of the effects of two textbook mediums. Eighty-one students participated in this study. Two textbook formats, hard copies and soft copies of the same textbook were used. A mixed-method research design was used for data collection with paired sample t tests adopted to compare the reading comprehension of two textbooks versions in immediate learning and summative learning on the same group of students, and a questionnaire and semi-structured interviews were employed to probe students’ perceptions. The results indicated that the participants performed no differently on the summative reading comprehension tests, but performed significantly better on immediate tests using the e-textbook. The questionnaire and the interviews showed that half of the respondents still preferred to use print compared to e-textbooks. This study concluded that e-textbooks were not yet positioned to replace print textbooks for university students in Taiwan. Nonetheless, pedagogically, since e-textbooks provide more interactive features than print, they should be considered an integral part of reading instruction.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".