Causal Discovery and Counterfactual Explanations for Personalized Student Learning

TL;DR

This paper applies causal discovery and counterfactual analysis to student performance data to identify causes and provide personalized educational recommendations, highlighting both potential and challenges of causal inference in education.

Contribution

It introduces the use of causal discovery techniques, specifically the PC algorithm, for understanding student performance and generating personalized advice.

Findings

Identified causal factors like test scores and math ability affecting performance.

Demonstrated the application of causal discovery methods to real student data.

Discussed limitations such as sample size and the need for domain knowledge.

Abstract

The paper focuses on identifying the causes of student performance to provide personalized recommendations for improving pass rates. We introduce the need to move beyond predictive models and instead identify causal relationships. We propose using causal discovery techniques to achieve this. The study's main contributions include using causal discovery to identify causal predictors of student performance and applying counterfactual analysis to provide personalized recommendations. The paper describes the application of causal discovery methods, specifically the PC algorithm, to real-life student performance data. It addresses challenges such as sample size limitations and emphasizes the role of domain knowledge in causal discovery. The results reveal the identified causal relationships, such as the influence of earlier test grades and mathematical ability on final student performance. Limitations of this study include the reliance on domain expertise for accurate causal discovery, and the necessity of larger sample sizes for reliable results. The potential for incorrect causal structure estimations is acknowledged. A major challenge remains, which is the real-time implementation and validation of counterfactual recommendations. In conclusion, the paper demonstrates the value of causal discovery for understanding student performance and providing personalized recommendations. It highlights the challenges, benefits, and limitations of using causal inference in an educational context, setting the stage for future studies to further explore and refine these methods.