Identifying Key Contextual Factors of Digital Reading Literacy Through a Machine Learning Approach
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".