RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
This addresses the need for efficient and interpretable early student performance prediction in education, offering a novel approach but with incremental improvements in method and domain-specific impact.
The paper tackled the problem of labor-intensive feature extraction in educational time series prediction by proposing a method using raw clickstreams with graph neural networks, achieving comparable or better accuracy than hand-crafted features and providing interpretable insights for interventions.
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/.