Bridging Gaps in Online Learning: A Systematic Literature Review on the Digital Divide
Bibliographic record
Abstract
This study explores the evolution and challenges of the digital divide in online education, intensified by the COVID-19 pandemic’s shift toward digital learning environments. A systematic literature review was conducted using three databases: Web of Science, Scopus, and the Education Resources Information Centre. The review focuses on identifying research designs, aims, and barriers within the literature from 2013 to 2023, highlighting the impact of the digital divide on access to educational technologies and digital literacy. A total of twenty-two articles were included in the dataset. The review reveals that despite advancements in online education technologies, significant disparities persist in access, digital skills, and educational outcomes, particularly affecting marginalized communities in both urban and rural settings. The study underscores the necessity for enhanced infrastructure, targeted educational policies, and inclusive teaching practices to bridge these gaps. Recommendations are provided for future research directions and practical implementations to mitigate the digital divide’s impact on educational equity.
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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 | 0.025 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".