The digital divide at school and at home: A comparison between schools by socioeconomic level across 47 countries
Bibliographic record
Abstract
Despite efforts to improve digital access in schools, a persistent digital divide is identified worldwide. Drawing on data from the 2018 Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessment (PISA) for 15-year-olds, I examine how students’ digital use for educational purposes (at school and at home) and their perceived digital competence differ between schools by socioeconomic status (SES) and vary across 47 countries. Using multilevel modeling, I find that the second-level digital divide between schools exists even among more developed societies. Students attending high-SES schools are more likely to use computers for schoolwork within and outside of schools, and have more digital competence than those attending low-SES schools. These differences remain substantial and statistically significant even when controlling for school-level resources. Moreover, the between-school digital divide in students’ digital competence is negatively associated with economic development and educational expenditures, and positively associated with income inequality. In conclusion, I discuss implications of the findings and highlight the importance of examining how schools with varying socioeconomic profiles provide different e-learning experiences for individual students, explained by the different institutional settings and cultural features of schools.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".