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Neural networks are ubiquitous in applied machine learning for education.Their pervasive success in predictive performance comes alongside a severeweakness, the lack of explainability of their decisions, especially relevant inhuman-centric fields. We implement five state-of-the-art methodologies forexplaining black-box machine learning models (LIME, PermutationSHAP,KernelSHAP, DiCE, CEM) and examine the strengths of each approach on thedownstream task of student performance prediction for five massive open onlinecourses. Our experiments demonstrate that the families of explainers do notagree with each other on feature importance for the same Bidirectional LSTMmodels with the same representative set of students. We use Principal ComponentAnalysis, Jensen-Shannon distance, and Spearman's rank-order correlation toquantitatively cross-examine explanations across methods and courses.Furthermore, we validate explainer performance across curriculum-basedprerequisite relationships. Our results come to the concerning conclusion thatthe choice of explainer is an important decision and is in fact paramount tothe interpretation of the predictive results, even more so than the course themodel is trained on. Source code and models are released athttp://github.com/epfl-ml4ed/evaluating-explainers.

Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs