Dropout insight: Educational risk dashboard with counterfactual explanations

Abstract

The prediction and prevention of students at risk of dropout are two of the most important challenges in the educational domain. Although some commercial predictive tools support at-risk estimation and provide explanations of the associated factors, none of them offer recommendations to address or reverse potential dropout cases. This paper proposes Dropout Insight as a prescriptive web-based interactive tool that automates the entire data-mining process to suggest specific decisions. It supports the loading and processing of student data, the selection of the best predictive model, and the visualization of results through interpretation techniques based on explainers. The tool provides a clear and visually intuitive interface that enables users to explore risk factors and simulate alternative scenarios, including instructors and other stakeholders, without prior knowledge of data mining. It offers not only traditional individual counterfactual explanations, but also novel group counterfactuals, which generate hypothetical clusters or groups of students with similar behavioral profiles. These groups help recover the largest possible number of at-risk students with less effort and cost by offering a single, shared recommendation for intervention. By integrating automated prediction tools with visual, explainable artificial intelligence methods and counterfactual reasoning, the tool becomes a highly valuable and innovative resource to support pedagogical decision-making and guide proactive educational policies aimed at preventing dropout.