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The paper focuses on identifying the causes of student performance to providepersonalized recommendations for improving pass rates. We introduce the need tomove beyond predictive models and instead identify causal relationships. Wepropose using causal discovery techniques to achieve this. The study's maincontributions include using causal discovery to identify causal predictors ofstudent performance and applying counterfactual analysis to providepersonalized recommendations. The paper describes the application of causaldiscovery methods, specifically the PC algorithm, to real-life studentperformance data. It addresses challenges such as sample size limitations andemphasizes the role of domain knowledge in causal discovery. The results revealthe identified causal relationships, such as the influence of earlier testgrades and mathematical ability on final student performance. Limitations ofthis study include the reliance on domain expertise for accurate causaldiscovery, and the necessity of larger sample sizes for reliable results. Thepotential for incorrect causal structure estimations is acknowledged. A majorchallenge remains, which is the real-time implementation and validation ofcounterfactual recommendations. In conclusion, the paper demonstrates the valueof causal discovery for understanding student performance and providingpersonalized recommendations. It highlights the challenges, benefits, andlimitations of using causal inference in an educational context, setting thestage for future studies to further explore and refine these methods.

Causal Discovery and Counterfactual Explanations for Personalized Student Learning