Interpretable machine learning insights into inequalities in access to online learning

McIntyre, Nora (2022) Interpretable machine learning insights into inequalities in access to online learning. McIntyre, Nora (ed.) In Machines with meaning: The potential of machine learning in educational research: A symposium convened at the Biennial Meeting of EARLI SIG27.

Record type: Conference or Workshop Item (Paper)

Abstract

Access to education is the first step to benefiting from it. Although cumulative online learning experience is linked academic learning gains (McIntyre, submitted), between-country inequalities mean that large populations are prevented from accumulating such experience. Low-and-middle-income status countries (LMICs) are affected by disadvantages in infrastructure such as internet access and uncontextualised learning content, and parents who are less available and less well-resourced than in high-income countries. COVID-19 has exacerbated the global inequalities, with girls affected more than boys in these regions.
Therefore, the present research mined online learning data to identify features that are important for access to online learning. Data mining of 54,842,787 initial data points from one online learning platform was conducted by partnering theory with data in model development. Following examination of a theory-led machine learning model, a data-led approach was taken to reach a final model. The linear regression model was regularised with the Lasso penalty to enable data-driven feature selection. Twenty-five features were selected to form an extreme gradient boosting model that underwent hyper-parameter tuning. All cross-validation adopted the grid search approach. The final model was used to derive Shapley values for feature importance.
As expected, country differences, gender, and COVID-19 were important features in access to online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of math ability, year of birth, session difficulty level, month of birth, and time taken to complete a session.

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