Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs
This work highlights a critical reproducibility issue in explainable AI for education, showing that interpretations of model decisions are highly dependent on the explainer used, which is incremental but important for practitioners.
The study implemented five black-box explainability methods on neural networks for predicting student success in MOOCs and found that explainer choice significantly impacts feature importance interpretation, with methods often disagreeing even more than differences across courses.
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 agree with each other on feature importance for the same Bidirectional LSTM models with the same representative set of students. We use Principal Component Analysis, Jensen-Shannon distance, and Spearman's rank-order correlation to quantitatively cross-examine explanations across methods and courses. Furthermore, we validate explainer performance across curriculum-based prerequisite relationships. Our results come to the concerning conclusion that the choice of explainer is an important decision and is in fact paramount to the interpretation of the predictive results, even more so than the course the model is trained on. Source code and models are released at http://github.com/epfl-ml4ed/evaluating-explainers.