CSDA-Vis: A (What-If-and-When) visual system for early dropout detection using counterfactual and survival analysis interactions

Student dropout is a major concern for universities, leading them to invest heavily in strategies to lower attrition rates. Analytical tools are crucial for predicting dropout risks and informing policies on academic and social support. However, many of these tools depend solely on automated predictions, ignoring valuable insights from professors, mentors, and specialists. These experts can help identify the root causes of dropout and develop effective interventions. This paper introduces CSDA-Vis, a visualization system designed to analyze the influence of individual, institutional, and socioeconomic factors on student dropout rates. CSDA-Vis facilitates the identification of actionable strategies to mitigate dropout by integrating counterfactual and survival analysis methods. Unlike traditional approaches, our tool enables decision-makers to incorporate their expertise into the evaluation of different dropout scenarios. Developed in collaboration with domain experts, CSDA-Vis builds upon previous visualization tools and was validated through a case study using real datasets from a Latin American university. Additionally, we conducted an expert evaluation with professionals specializing in dropout analysis, further demonstrating the tool's practical value and effectiveness.