Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs. (arXiv:2207.00551v1 [cs.LG])

Neural networks are ubiquitous in applied machine learning for education.
Their pervasive success in predictive performance comes alongside a severe
weakness, the lack of explainability of their decisions, especially relevant in
human-centric fields. We implement five state-of-the-art methodologies for
explaining black-box machine learning models (LIME, PermutationSHAP,
KernelSHAP, DiCE, CEM) and examine the strengths of each approach on the
downstream task of student performance prediction for five massive open online
courses. Our experiments demonstrate that the families of explainers do not …

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