Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

TL;DR

Interpret3C introduces an interpretable neural network-based clustering method that adaptively selects features for each student, improving the relevance and interpretability of clusters in large-scale online education data.

Contribution

It presents a novel unsupervised clustering pipeline that incorporates individualized feature selection via interpretable neural networks, addressing the challenge of high-dimensional data in education.

Findings

Enhanced interpretability of student clusters.

Effective feature selection for individual students.

Scalable approach demonstrated on MOOC data.

Abstract

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging with high-dimensional data. Existing clustering approaches often neglect individual differences in feature importance and rely on a homogenized feature set. Addressing this gap, we introduce Interpret3C (Interpretable Conditional Computation Clustering), a novel clustering pipeline that incorporates interpretable neural networks (NNs) in an unsupervised learning context. This method leverages adaptive gating in NNs to select features for each student. Then, clustering is performed using the most relevant features per student, enhancing clusters' relevance and interpretability. We use Interpret3C to analyze the behavioral clusters considering individual feature importances in a MOOC with over 5,000 students. This research contributes to the field by offering a scalable, robust clustering methodology and an educational case study that respects individual student differences and improves interpretability for high-dimensional data.