Boosters and Blockades to Online Learning: Machine Learning Analysis of Disadvantage and Deprivation in Access to One Online Game Seminar

This talk examines the roles of the following features in predicting learner access to online learning: location in low-and-middle-income country, within-country deprivation, gender, and academic age (or mathematical learning age).

Using regression-based machine learning with extreme gradient boosting, the research demonstrates that the longstanding and systemic effects of world-wide disparities emerge at the macro- and intra-individual levels of online learning outcomes, when measured as educational access over time.

An implication for practice is that online learning providers need to seek ways to combat the challenges to educational access among the most deprived. An implication for research is the value of all levels of research analyses for highlighting priorities in social justice.