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/.

本文通过利用图神经网络的方法，对不规则多元时间序列进行建模，比较使用手工制作的特征和原始时间序列clickstreams能否在学生表现预测方面获得相同或更好的准确性，以及在原始时间序列模型的可解释性方面扩展概念活化向量，并在23个海量 MOOCs 中进行了实验分析，成功实现了对学生表现预测和干预的课程支持。

RIPPLE：面向教育的基于概念的原始时间序列模型解释