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RIPPLE: Concept-Based Interpretation for Raw Time Series Models in Education
Machine Learning · Submitted 2 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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/.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Time series is the most prevalent form of input data for educational prediction tasks. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | The vast majority of research using time series data focuses on hand-crafted features, designed by experts for predictive performance and interpretability. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s2 | However, extracting these features is labor-intensive for humans and computers. | background 0.56 | 0.07 | 0.02 | asserted 1.00 |
| s3 | 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. | contribution 0.54 | 0.83 | 0.22 | asserted 0.76 |
| s4 | Furthermore, we extend concept activation vectors for interpretability in raw time series models. | contribution 0.57 | 0.91 | 0.06 | asserted 0.95 |
| s5 | We analyze these advances in the education domain, addressing the task of early student performance prediction for downstream targeted interventions and instructional support. | contribution 0.85 | 0.69 | 0.06 | asserted 0.54 |
| s6 | 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. | result 1.00 | 0.97 | 0.85 | asserted 1.00 |
| s7 | Source code: https://github.com/epfl-ml4ed/ripple/. | contribution 0.70 | 0.25 | 0.02 | unclear 0.91 |
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Original 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/.
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There exists a large body of approaches on student success prediction in MOOCs, e.g., random forests [ref] , logistic regression [ref] , or neural networks [ref] .
Next-Term Student Performance Prediction: A Recommender Systems Approach · Introduction · 0.96
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