Recommendation systems (RS) have been used in many scenarios, from entertainment to health. Inside the RS area, Educational Recommendation Systems (ERS) are becoming popular, been used for different types of recommendations such as recommending materials, exercises, and learning paths. As ERS works in a different scenario of classics RS, ERS requires specific evaluation metrics. However, the task of evaluating ERS is difficult once the educational field has its features to be analyzed. To help other researchers in this field, this work presents a systematic mapping on methods used for evaluating ERS. This study analyzed 91 papers of the last five years and provide an overview of the main methodologies, subject, metrics, and trends in the evaluation of ERS.

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O presente trabalho apresenta o uso de técnicas de Mineração de Dados com foco na identificação de desigualdades sociais a partir da análise do desempenho dos estudantes concluintes do ensino médio que prestaram o Exame Nacional do Ensino Médio - ENEM no ano 2019. O uso de algoritmos de Clusterização e de Regras de Associação permitiu mapear as variáveis determinantes no desempenho dos estudantes bem como caracterizar dois grupos bem definidos a partir dos resultados obtidos nas cinco avaliações do exame.