Development of an Adaptive E-Learning Model for Personalized Learning Path Recommendation Using Ant Colony Optimization and Hybrid Filtering
Keywords:
Adaptive, Ant colony optimization, E-learning model, Hybrid filtering, PersonalizedAbstract
The majority of traditional e-learning models often fail to cater to individual learners’ needs due to limited parameter flexibility across diverse populations and the scope of adaptive courses. To address these challenges, this research developed an adaptive e-learning model using Ant Colony Optimization (ACO), collaborative filtering, and content-based filtering. The ACO was used for adapting the learning contents and activities, while also dynamically adjusting the learning path. K-Nearest Neighbor (KNN) was used for e-learning styles and teaching strategies to generate courses for learners. The Felder-Silverman Learning Style Model is employed to identify and accommodate different learning styles, ensuring a tailored educational experience. The implementation of the model is done using Python and PHP frameworks. An online survey was conducted with 500 undergraduate students from various academic disciplines to evaluate the developed model. The KNN algorithm achieved the best performance with an R² of 0.986 and an MAE of 0.012, indicating both high accuracy and minimal prediction error. A direct comparison with the benchmark study reveals that our models outperform their counterparts, with the KNN model achieving (0.012, 0.986) against theirs (0.013, 0.875). It demonstrates significant improvements in learner satisfaction and academic performance. This research contributes to e-learning by offering a flexible, adaptive solution that enhances educational outcomes through personalized learning experiences.
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