RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education

TL;DR

This paper introduces RIPPLE, a graph neural network-based approach for raw time series in education, achieving high accuracy and interpretability without manual feature extraction for student performance prediction.

Contribution

RIPPLE extends concept activation vectors to raw time series models, enabling interpretable predictions in educational data without feature engineering.

Findings

Outperforms state-of-the-art baselines on MOOC data

Provides interpretable insights for personalized interventions

Works with raw clickstream data without feature extraction

Abstract

Time series is the most prevalent form of input data for educational prediction tasks. The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. However, extracting these features is labor-intensive for humans and computers. In this paper, we propose an approach that utilizes irregular multivariate time series modeling with graph neural networks to achieve comparable or better accuracy with raw time series clickstreams in comparison to hand-crafted features. Furthermore, we extend concept activation vectors for interpretability in raw time series models. We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. Our experimental analysis on 23 MOOCs with millions of combined interactions over six behavioral dimensions show that models designed with our approach can (i) beat state-of-the-art educational time series baselines with no feature extraction and (ii) provide interpretable insights for personalized interventions. Source code: https://github.com/epfl-ml4ed/ripple/.