Reading Comprehension and Behavior in Children Using E-books vs. Printed Books
Bibliographic record
Abstract
The purpose of this research is to investigate the influence that personalized, gamified, and PDF electronic reading practices have on the attitudes which fifth-grade students possess toward e-reading experiences, as well as how these stances affect the students' motivation and reading comprehension while they are learning English as a second/foreign language (EFL). For the purpose of the study, there were a total of 84 fifth-grade kids from public schools in Greece, who participated. These students were split up into three different experimental groups and a control one. Participants in the experimental groups read throughout the treatment period according to a preset schedule using one of three diverse electronic reading formats (PDF, gamified, or customized), whilst participants in the control group read utilizing a paper guided reading plan. The participants' experiences playing video games online were analyzed via a technique called the quasi-experimental approach. According to the findings of the research, the experimental group and the control group did not significantly vary from one another in terms of their levels of reading comprehension. On the other hand, in comparison to the participants in the control group, those who took part in the experiments reported having more favorable sentiments regarding their electronic reading experiences and were more inspired to read. As indicated from the research findings, kids may experience an increase in their desire to read when they use electronic gadgets. This study has implications for educators and policymakers as they consider incorporating digital reading practices into their teaching methods, particularly when it comes to improving students' motivation to read.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".