PREMUCC: Design proposal for a microservices architecture for the development of a context-aware dropout prediction system

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Introduction: This article presents the PREMUCC system, a proposal based on microservices to predict context-aware college dropout in higher education. Student attrition is a concerning issue addressed by this innovative solution, enabling personalized support and informed decision-making. Objective: The objective of the PREMUCC system is to anticipate university dropout through the use of machine learning techniques and a microservices architecture, providing personalized support to students and facilitating educational management. Method: The proposal for PREMUCC was based on analyzing dropout prediction systems and microservices architectures. Technologies and data analysis processes were identified to ensure system efficiency and accuracy. Results: The PREMUCC system integrates six microservices encompassing user authentication, previous studies analysis, psychological testing, grade tracking, financial evaluation, and the prediction system. Students access personalized information to improve their academic performance. Conclusions: The PREMUCC proposal represents a significant advancement in mitigating college dropout. Its focus on microservices and modern technologies enables closer tracking of each student's context, supporting informed decision-making. Its implementation could enhance the educational system, saving resources, time, and effort. It is proposed to strengthen the architecture and evaluate its effectiveness in real higher education settings.