# Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

arXiv: https://arxiv.org/abs/2407.11979 · HTML: https://mlsanity.com/paper/2407.11979/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 6 of 6)

- [XAI Beyond Classification: Interpretable Neural Clustering](https://mlsanity.com/paper/1808.07292/) (2018)
- [Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs](https://mlsanity.com/paper/2207.00551/) (2022)
- [The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations](https://mlsanity.com/paper/2307.00364/) (2023)
- [Intrinsic User-Centric Interpretability through Global Mixture of Experts](https://mlsanity.com/paper/2402.02933/) (2024)
- [Meta Transfer Learning for Early Success Prediction in MOOCs](https://mlsanity.com/paper/2205.01064/) (2022)
- [Analyzing Student Strategies In Blended Courses Using Clickstream Data](https://mlsanity.com/paper/2006.00421/) (2020)

## Built on by (most cited first, 1 of 1)

- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)

## Abstract (arXiv)

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.