# SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration
## 1 Introduction  
Education is a crucial variable determining the level of inequality in a society, and policymakers usually justify higher education spending as a highly effective tool for reducing income inequality [1]. However, given the low graduation rates, the existence of some phenomena that directly affect the efficiency of the education system is evident. The deficiency of the educational system could be the result of the two phenomena of retention and evasion. Retention is extension beyond the expected duration of the course, while evasion is when the student drops out before the final year of an educational course in which they have enrolled. According to the Organization for Economic Cooperation and Development (OECD), in recent years, European dropout rates were somewhere in the range between 30% and a half, while in the United States the dropout rate for undergraduate study was 40%. Among Latin American countries, Brazil's dropout rates surpassed 54%, Costa Rica's 50%, Colombia's 40%, and Peru's 38% [2]. This situation worsened with the onset of the COVID-19 pandemic, which also particularly highlighted economic inequality. According to domain experts, the interruption of studies is a long-standing problem and is \"multi-causal and not only economic\", since it is also attributable to family, vocational, and academic demands. Research in this area has commonly focused on identifying who [3] will leave college and when [4; 5]. In general, these techniques allow the identification of the association between different patterns and student dropouts. Several studies have considered improving the sensitivity of the predictive model by using different information such as historical academic factors [6; 7; 8; 9], socioeconomic factors [3; 10], curricular design [11], scholarships [7], and the residence and origin of the student [10]. However, although these studies could identify dropout patterns, they did not help decision makers decide what they should do next in order to prevent dropouts. In this sense, it is necessary to implement methodologies that allow the effects of different actions on a student or group to be explored. For example, if preventing a university student's dropping out requires changing, e.g., the GPA on mandatory courses, the model is potentially biased. In this context, counterfactual explanations provide alternatives for various actions by showing feature-perturbed versions of the same instance [12; 13]. The objective of the counterfactual analysis is to answer the question: _How does one obtain an alternative or desirable prediction by altering the data just a little bit?_[14]. For instance, for a dropout student in the data set and in contrast to a machine learning model based on the real values, counterfactual explanations provide synthetic sample recommendations for what to do to achieve a required goal (preventing student dropout). Broadly speaking, counterfactual explanations attempt to pass a student from the dropout space to the non-dropout space (Figure 1) by changing some of the values of the real feature vector. Let us illustrate this with an intuitive example with a student dropout application. _Imagine a decision maker analyzing a student's characteristics. Unfortunately, this student is at risk of dropping out of the university. Now, the decision maker would like to know why. Specifically, the user would like to know what should be different for the student, to reduce the risk of dropping out. A possible explanation might be that the student would have been a non-dropout if the student had obtained 8.5 (0-10 scale) as a grade for mandatory courses and had reduced absenteism (CF1-CF3)._ In other words, counterfactual explanations provide \"what-if\" explanations of the model output [13]. Counterfactual explanations are highly popular, as they are intuitive and user-friendly [12; 15]. However, an interactive visual interface is necessary for the general public who do not have prior knowledge of machine learning models.