Education is very important for a society’s economic, social, and cultural growth. However, educational systems still have structural problems that make it hard for students to stay in school and achieve academic success. One of the most significant problems in education is student dropout. This situation is especially impactful in high school, where its effects extend beyond the school and into long-term social and economic outcomes. This paper proposes an approach to identify data-driven indicators of dropout risk by using supervised learning and optimization methods. Our proposal consists of a supervised feature-weighted metric learning strategy that improves class separability in distance-based classifiers by reweighting features based on label information. To achieve the best possible k-nearest neighbors classification accuracy, we formulate metric learning as an optimization problem. Moreover, to optimize our proposal, we considered population-based, gradient-free metaheuristics. Furthermore, our proposed method preserves the original feature space to improve neighborhood relationships in contrast to traditional preprocessing or dimensionality reduction methods, which are important for educational outcomes. Actual school records from a high school in Ciudad Madero, Tamaulipas, Mexico, were used to conduct the experimentation to assess our proposal. Based on the experimental results, we observe an improvement in classification performance, with accuracy increasing from about 0.87 to 0.98. For statistical support, we applied a nonparametric Friedman test, which showed that these improvements are statistically significant. Hence, our proposal could be a useful and scalable method for educational data and support strategies for early identification of students at risk of dropout.

Machine learning has a wide range of applications, including classification, which categorizes elements based on their characteristics. This paper addresses the challenge of optimizing attribute weighting while assessing two crossover operators on differential evolution optimization and increasing the performance of the k-nearest neighbors classification algorithm (KNN). We use a differential evolution optimization method and assess the performance of both the differential evolution and the harmony crossover operators. Finally, the optimization method uses the accuracy of a KNN classification algorithm as a fitness function. The results show that the proposed method significantly enhances the KNN performance while proposing an alternative for other classification models such as neural networks and Random Forest.