From Data to Insight: Management, Representation, and Evaluation of Student Engagement and Personalization in Academic Information Ecosystems

In the context of higher education, student engagement and personalized learning are increasingly being recognized as critical factors for academic success and effectiveness. This research explores how academic information ecosystems, comprising learning management systems, student information systems, and research analytics platforms, can be leveraged to represent, manage, and evaluate student engagement and personalization strategies. The aim is to enhance evidence-based decision making in universities by providing tools and metrics for monitoring and improving students’ learning experiences. This study contributes to the advancement of research data management practices in academic institutions, aligned with contemporary trends in open science, data-driven governance, and learning analytics. By analyzing student participation in online distance learning and examination processes, we identify key deterrents to engagement and tailor announcements, recommendations, and feedback. We utilized a hybrid modeling technique that combines individual student profiles and community-based groupings to predict student performance and failure risk with high accuracy. Our findings indicate that personalized interventions significantly improve student success rates across multiple programming courses from C and C++ to advanced mobile programming. Additionally, the research highlights students’ willingness to provide anonymous feedback on academic processes, emphasizing the value of incorporating their opinions into the system design. The proposed system facilitates continuous quality improvement in teaching and assessment, fostering a supportive learning environment and encouraging active student participation.