  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.
