Towards A Model For Educational Resource Recommendation Based On Cognitive Load

Abstract

The advent of digital learning platforms has necessitated the development of intelligent systems that can provide personalized educational experiences.This paper introduces a model for recommending educational resources based on cognitive load analysis, aimed at optimizing learning efficiency by tailoring content to individual cognitive capacities.The model integrates learner profiles, real-time cognitive load assessment, content analysis, and a recommendation engine utilizing machine learning algorithms.It leverages the principles of Cognitive Load Theory and Information Processing Theory to design educational materials and experiences that align with human cognitive architecture.The implementation of this model in an online learning platform demonstrated significant improvements in learner engagement and learning outcomes.Specifically, the use of Support Vector Machines for cognitive load assessment achieved an accuracy of 89.5%, and the recommendation engine showed high precision and recall in suggesting relevant re-sources.The results indicated an average improvement of 15% in test scores among learners who used the recommended resources.These findings highlight the potential of cognitive load analysis in enhancing personalized learning experiences, suggesting that such models can be instrumental in creating more adaptive and responsive digital educational environments.