Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes

Divya R Krishnan, Sandarbh Yadav, Pritha Biswas, Md Shaik Amzad Basha, L. Prathiba · 2025

Summary. This study uses machine learning models to predict outcomes of digital literacy programs in rural education settings. Researchers tested multiple regression approaches from linear regression to advanced ensemble methods like XGBoost and Stacking, evaluating their accuracy using MSE and R-squared metrics. Ensemble techniques with multiple features performed best, and the findings suggest machine learning can help design customized digital education solutions for rural communities.

Read the original

@article{krishnan-2025-bridging-rural-digital-divide-machine,
  title   = {Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes},
  author  = {Divya R Krishnan and Sandarbh Yadav and Pritha Biswas and Md Shaik Amzad Basha and L. Prathiba},
  year    = {2025},
  doi     = {10.1109/i2itcon65200.2025.11210709},
  url     = {https://doi.org/10.1109/i2itcon65200.2025.11210709}
}

TY  - JOUR
TI  - Bridging the Rural Digital Divide: Machine-Learning-Driven Predictive Modeling of Digital Literacy Program Outcomes
AU  - Divya R Krishnan
AU  - Sandarbh Yadav
AU  - Pritha Biswas
AU  - Md Shaik Amzad Basha
AU  - L. Prathiba
PY  - 2025
DO  - 10.1109/i2itcon65200.2025.11210709
UR  - https://doi.org/10.1109/i2itcon65200.2025.11210709
ER  - 

Details

DOI

10.1109/i2itcon65200.2025.11210709

Countries

India

Regions

Asia

Categories

broadband-and-digital, education, general-innovation

Added

2026-05-01