Perceptions and Preferences of Digital and Printed Text and Their Role in Predicting Digital Literacy
Bibliographic record
Abstract
This study explored the relationships between the reading of digital versus printed text among 1,206 South Korean high school students in grades 9 through 12. The Test of Silent Contextual Reading Fluency (2nd ed.), the Digital Propensity Index, and the Reading Observation Scale were among the instruments used to measure reading proficiency and digital propensity. Statistical analysis was comprised of a paired sample t test to compare students’ reading perceptions of digital and printed text; independent sample t tests were used to explore reading preferences and the relationships between digital and printed text; and a multiple linear regression was used to explore digital propensity based on reading behaviors. Among the results, students were found to have higher positive perceptions of the reading of printed text; reading preference depended on the purpose for reading (e.g., learning versus entertainment); and significant mean differences were found among students’ reading scores and digital propensity regarding preferences between the reading of digital and printed text. Although much more research is needed before any definitive conclusions can be drawn, the findings suggest several ways to achieve student literacy competency in the use of digital and printed text, while also pointing to additional factors that influence perceptions and behaviors among these two formats.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".