Accelerating online learning: Machine learning insights into the importance of cumulative experience, independence, and country setting

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Cumulative experience is important for developing expertise through in-person learning, along with country setting and gender, but evidence is limited the role of these features in online learning. Yet, COVID-19 has catalysed the centrality of online learning, such that the efficacy of online learning is now highly relevant. Although the Pandemic triggered a surge of self-report and literature review research on stakeholder perceptions of online learning, less educational research has used big data to understand online learning. Therefore, the present research mined online learning data to identify features that are important for developing expertise in 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-six 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, cumulative experience, country differences, low-and-middle-income country status, and COVID-19 were important features for developing expertise through online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of meta-cognition and independent learner behaviour. Surprisingly, no male advantage was found in the potential for expertise development through online learning.