Abstract
Artificial Intelligence in Education (AIED) offers numerous applications, including student success prediction, which assists educators in identifying the customized support required to improve a student’s performance in a course. To make accurate decisions, intelligent algorithms utilized for this task take into account various factors related to student success. Despite their effectiveness, decisions produced by these models can be rendered ineffective by a lack of explainability and trust. Earlier research has endeavored to address these difficulties by employing overarching explainability methods like examining feature significance and dependency analysis. Nevertheless, these approaches fall short of meeting the unique necessities of individual students when it comes to determining the causal effect of distinct features. This paper addresses the aforementioned gap by employing multiple machine learning models on a real-world dataset that includes information on various social media usage purposes and usage times of students, to predict whether they will pass or fail their respective courses. By utilizing Diverse Counterfactual Explanations (DiCE), we conduct a thorough analysis of the model outcomes. Our findings indicate that several social media usage scenarios, if altered, could enable students who would have otherwise received a failing grade to attain a passing grade. Furthermore, we conducted a user study among a group of educators to gather their viewpoints on the use of counterfactuals in explaining the prediction of student success through artificial intelligence.
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Farzana was supported through an Australian Government’s RTP Scholarship.
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Afrin, F., Hamilton, M., Thevathyan, C. (2023). Exploring Counterfactual Explanations for Predicting Student Success. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_44
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