AN ONTOLOGY-BASED FRAMEWORK FOR STUDENT PROFILING AND CONTENT PERSONALIZATION IN HIGHER EDUCATION

As distance education (DE) and virtual learning environments (VLEs) become increasingly integrated into higher education, the need for pedagogical models that support student autonomy has grown. Many existing DE programs rely on standardized, linear instructional models that fail to account for the diverse academic backgrounds and learning needs of individual students. This paper explores the implementation of self-paced learning (SPL) as a solution to these challenges, proposing a framework that uses ontologies to manage student data and personalize learning pathways.

Drawing on a systematic literature review, the authors identify several critical components for successful SPL in e-learning:

• Student Profiling: Moving beyond basic academic records to capture granular data on learning behaviors, interests, and proficiency gaps.
• Content Granularity: Balancing the size of learning objects to ensure content is flexible enough for personalization without becoming overly fragmented or incoherent.
• Adaptive Content: Utilizing AI and recommendation systems to suggest learning pathways tailored to individual student profiles.
• Collaborative Learning: Implementing peer assessment and collaborative content creation to foster engagement, even in asynchronous environments.
• Feedback and Monitoring: Leveraging automated log analysis and timely feedback to maintain student engagement and mitigate the risks of procrastination associated with flexible learning schedules.

The authors argue that traditional student records are insufficient for modern personalization. They propose an ontology-based architecture designed to store and manage complex information about the educational process. By using ontologies, institutions can better represent the relationships between student competencies, learning objects, and pedagogical goals. This structure allows for more informed decision-making regarding content delivery and provides a foundation for AI-driven adaptive learning systems that can support students without replacing the essential role of the instructor.