A Framework For Adaptive E-Learning Module Recommendation Using Intuitionistic Fuzzy Sets And Euclidean Distance

Author(s)	Dr. Senthilkumar A, Ms. Vinupriya P
Country	India
Abstract	Intuitionistic Fuzzy Set which extends classical fuzzy sets by incorporating both membership and non-membership degrees along with hesitation, provide a richer way to model uncertainty. This paper introduces an innovative framework for intelligent E-learning recommendation systems by integrating intuitionistic fuzzy sets (IFS), Euclidean distance metrics, and pattern recognition techniques. The proposed model effectively captures both learner’s confidence (membership) and hesitation (non-membership) levels, providing detailed representations of individual learning profiles. The minimal Euclidean distance identifies the most similar student profiles enabling precise personalized recommendations and data driven instructional decisions.
Keywords	Intuitionistic fuzzy sets, Euclidean distance, E-learning, pattern recognition, personalized recommendation.
Field	Mathematics
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-25
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.51948
Short DOI	https://doi.org/g9vpx5

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