Digital Inequality and Learning Gains: Evidence from Digital Literacy Training Environments

Abstract

This study examines the relationship between digital inequality and learning gains within digital literacy training environments, with a focus on understanding whether socioeconomic and engagement factors influence skill improvement. A quantitative, explanatory research design was adopted using a secondary dataset obtained from Kaggle (Digital Literacy Education Dataset). The final sample consisted of 788 observations after data preprocessing. Learning gain was measured as the difference between post-training and pre-training digital literacy scores. Statistical analyses were conducted using Python, including descriptive statistics, paired-samples t-test, ANOVA, and multiple linear regression. The results indicate significant improvements in digital literacy following training, with a large increase in post-training scores. However, no statistically significant differences in learning gains were observed across household income groups. Regression analysis revealed that most demographic, socioeconomic, and engagement variables were not significant predictors, and the model demonstrated low explanatory power. The study contributes to the literature by focusing on learning gains within digital literacy training environments rather than traditional academic outcomes, providing evidence that structured interventions can mitigate the effects of digital inequality in short-term skill development.