Technological advancements have brought abundant freedom to our lives. In an educational context, however, the technology utilization is still relatively low despite recent developments on various learning platforms such as e-learning, mobile learning, MOOCs, and social networks. The contemporary technological advancement in smart gadgets enables us to bring learning resources with appropriate content format to the learners at the right time in the right learning situation. Yet there remains a need for an adaptive study management solution that would apply data mining algorithms to assist university students both before and during their studies in a personalized manner. This assistance can be of many kinds, such as campus orientation to new students, course curriculum recommendations, and customization of study paths. In this paper, we present the concept and an initial implementation the Adaptive Study Management (ASM) platform that aims at facilitating a university student’s academi c life in different phases by tracing the student’s activities and providing personalized services, such as a course curriculum recommendation, based on their behavior and achievements during a period. The ASM platform creates a profile for the student based on their achievements and competencies. Consequently, the platform aims to grant freedom to students on their study management, eases teachers’ workloads on assessing students’ performance, and assists teachers and administrators to follow up students and dropouts. The goal of this platform to increase graduation rates by personalizing study management and providing analysis services, such as dropout prediction.