Predicting Digital Literacy Gains in Rural Contexts Using Multidimensional Data: A Machine Learning Approach
Bibliographic record
Abstract
In low-resource educational contexts, the early identification of at-risk learners remains critical to preventing dropout and ensuring equitable skill acquisition. This study develops a predictive modeling framework to classify learner progression in digital literacy training programs targeting rural and semi-rural populations, across three domains: computer use, Internet navigation, and mobile literacy. Using a dataset of 1000 learners, six supervised classification algorithms were implemented via cross-validation, with Support Vector Machines (SVM) achieving the highest accuracy ($97 \%$) with strong recall for both no-risk ($97 \%$) and at-risk learners ($96 \%$). Feature analysis revealed that initial skills are the strongest predictor of progression, while contextual factors such as geographical disparities and household income level also significantly influence learning trajectories. These models enable the early identification of at-risk learners and the establishment of personalized support systems to reduce dropout and advance digital equity in marginalized communities.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".