Identifying Key Contextual Factors of Digital Reading Literacy Through a Machine Learning Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Few of previous reading studies comprehensively examined the contributing factors of students’ digital reading literacy. To fill this gap, based upon the ecological perspective, this study aims to investigate which factors from the student, home, and school context are more important in discriminating high-performing digital readers from non–high-performing digital readers. The data of the Progress in International Reading Literacy Study 2016 with 74,692 Grade 4 students from 14 countries and economies was analyzed using the machine learning approach of support vector machine with recursive feature elimination. Results showed that except print reading levels, students’ reading self-efficacy, home resources for learning, talking about what have read in class, and the number of books in the home are the most influential contextual factors contributing to the high performance of digital readers. The selected 20 key contextual factors render a high prediction power for discriminating digital readers. Our findings show that, in general, home-related factors have overarching influences on children’s digital reading development; at the school level, instruction-related features are more influential than school characteristics.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it