Gender Differences in Chinese EFL Learners’ Comprehension When Reading Across Mediums
Bibliographic record
Abstract
As the reading medium shifts from paper to digital devices, there is a notable change in readers’ habits, moving from traditional printed formats to electronic forms. While extensive research has been conducted on gender differences in traditional print reading, studies focusing on gender disparities in mobile reading comprehension are rather limited. This research employed cross-sectional quantitative methods to explore gender differences in comprehension through both print and smartphone-based reading tests. Data was gathered from 190 undergraduates at a local university in China through reading comprehension tests. Participants’ reading results were analyzed using SPSS software for comparison. The findings indicate no significant difference in reading achievements between male and female participants on both paper and smartphone platforms. However, the study found positive trends in female students’ performance on both mediums. The disparity in the mean difference between male and female students’ reading scores was larger in smartphone-based tests, which indicates that digital mediums could either enhance reading performance in female students or potentially have a negative impact on male students.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".