The Influence of Native Language and Text Presentation on Reading Comprehension on Smartphones
Bibliographic record
Abstract
Whilst there is increasing usage of small-screen devices such as mobile phones shape the way of acquiring information that was different from the traditional way of static text on paper. Literature indicates not only different text presentations influence people reading comprehension level but also language contributes to reading abilities. However, the extent to which recent text presentation formats influence comprehension remains unclear, as does the interaction between text presentation and language proficiency among native English speakers and Chinese readers with second language English. This study aimed to examine the effects of different text presentations and first or second language readers on English reading comprehension. Participants with volunteers (N = 51) were grouped independently of either Chinese or English readers and presented with four different text presentations. A bespoke reading comprehension task was used to measure participants' comprehension levels and data was analysed by factorial mixed measure ANOVA. The findings demonstrated that overall, first language readers had better comprehension compared to Chinese readers when engaging in English reading. However, there is no significant difference in comprehension between different text presentations. Theoretical factors contributed to the current study and use to explain the phenomenon of the current findings, while also acknowledging the potential methodological limitations related to culture and individual differences. Future research should consider these factors to get deeper insight. Despite these limitations, the study offered real-life applications with benefits to both technological science and educational psychology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".