A Neutrosophic Decision-Support Framework for Adaptive Learning Pathways in Digital Education Platforms.

Personalized learning pathways in digital education platforms have become essential for addressing the unique needs and behaviors of individual learners. However, traditional adaptive systems often fail to account for the uncertainty, ambiguity, and inconsistency inherent in educational data. This paper proposes a novel neutrosophic decision-support framework that models learner profiles using truth (T), indeterminacy (I), and falsity (F) scores derived from student interaction and performance data. Utilizing the Open University Learning Analytics Dataset (OULAD), we compute neutrosophic learner vectors based on assessment outcomes, engagement patterns, and virtual learning environment (VLE) activity. A rule-based decision engine then recommends adaptive learning pathways-ranging from remedial to advanced-by interpreting the T/I/F distributions through a neutrosophic logic framework. Experimental results demonstrate that the proposed model enhances pathway assignment accuracy and provides better support for learners with incomplete or uncertain data compared to traditional fuzzy and crisp models. The neutrosophic approach also ensures interpretability and flexibility, making it well-suited for real-world educational platforms aiming to achieve adaptive learning at scale. [ABSTRACT FROM AUTHOR]