Adaptive E-Learning Environments: A Methodological Approach to Identifying and Integrating Multi-layered Learning Styles

Abstract

E-learning environments represent digital platforms designed to facilitate online learning experiences. Recognizing the diverse learning preferences of individuals, the need for identifying and integrating multi-layered learning styles within these environments is paramount. Existing approaches often face limitations in accurately capturing and accommodating these intricate learning styles. To address these challenges, this study proposes a novel approach. Firstly, the integration of word embedding-based feature extraction transforms textual data into continuous vector representations, enhancing feature robustness and meaningfulness. Secondly, leveraging deep belief networks (DBNs) allows for the automatic learning of hierarchical data representations, improving model performance by capturing complex patterns. Thirdly, the DBN-based model accommodates multi-layered learning styles, including visual, auditory, kinaesthetic, and read/write preferences, offering personalized recommendations to learners. Lastly, the proposed approach is scalable and generalizable, capable of handling large datasets and diverse educational contexts, thus enhancing the efficacy of e-learning platforms. This innovative approach demonstrates promising advancements in learning style identification within e-learning environments, providing personalized guidance to learners and improving the overall effectiveness of online education. The proposed Deep Belief Network (DBN) model exhibits a significant average increase in accuracy compared to other methods implemented in Python. With an accuracy of 99.5%, the DBN model surpasses the accuracy of the K-Nearest Neighbours (KNN) and Random Forest (RF) models by 25.4 and 3.3%, respectively. This substantial improvement underscores the superiority of the proposed DBN model in learning style identification.