Hybrid Attention-Enhanced Deep Learning for Adaptive Digital Literacy Prediction in College Learners

Abstract

Digital literacy has become a pillar of rural revitalization efforts across the globe, and the creation of smart, adaptive systems to forecast and tailor learner outcomes constitutes a major area of research gap. This paper presents a new Hybrid Attention-Enhanced Deep Learning Network (HAE-DLN) a multi-task deep learning model that predicts overall scores on digital literacy (regression), classifies learners into proficiency levels (3-class), and classifies above/below-median learners (binary classification). The HAE-DLN is built upon a Feature Attention Module (FAM), Residual Dense Blocks (RDB), as well as a Multi-Task Learning Head (MTL), whilst being trained on a complete synthetic dataset of 1,000 rural learners, covering 23 demographic, behavioral, engagement, and outcome variables, in one end-to-end trainable framework. The first comparison on the baseline with the Random Forest and the Gradient Boosting regressors in their tuning with the help of the GridSearchCV proves that the proposed model has a better predictive power. Findings indicate that post-training scores, quiz performance and engagement efficiency are most effective predictors of digital literacy outcomes. The results have a direct implication on the design of adaptive, personalized digital literacy initiatives in rural and underserved people.