Which is Better: E-Book or Printed Book? A Meta-Analysis of Educational Materials in Language Learning
Bibliographic record
Abstract
This study evaluated the effect of electronic books (e-books) on improving language learning outcomes, particularly in core proficiencies such as speaking, writing, and listening, which are identified as key areas of improvement. Using a meta-analysis approach, this research employed a randomized model with experimental and control groups. Data selection followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) scheme, based on inclusion and exclusion criteria, using the Scopus database. Statistical analyses included tests for heterogeneity, publication bias, total effect size, and moderator variables using analysis of variance. The findings indicate that e-books positively influence the learning process as compared to printed books, with a medium standardized effect size of 0.5. Among languages, Arabic and Turkish benefitted the most from e-book use, while native language learning showed greater improvements than second-language acquisition. Interactive e-books demonstrated significantly higher effectiveness compared to non-interactive ones, though their impact on reading skills was relatively smaller. These results underscore the value of e-books as tools for enhancing language learning. Future efforts should focus on developing interactive e-books tailored to specific languages and proficiency needs to maximize educational potential.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".