A personalised adaptive e-learning systems based on deep learning approaches: A critical interpretation of learning style models

Abstract

A possible approach for enhancing the efficacy of online learning environments and addressing the challenge of e-learning personalisation is adaptive e-learning. Deep learning-based approaches have gained significant attention in adaptive education systems to impart personalised adaptive education to classify learner types. These approaches utilise an automatic means to recognise dynamic learning styles to enhance the e-learning experience. In this article, the authors present a critical interpretative approach to explore different learning style models, in order to develop a suitable framework that will assist in identifying learning styles. This framework can be instrumental in delivering personalized adaptive learning, primarily grounded in deep learning approaches. The findings indicate that the Felder–Silverman’s learning style model is considered the most suitable model for providing adaptivity. It is well-suited for identifying learners' learning styles in e-learning environments, ultimately optimizing the individual learning experience. Future research should focus on empirically evaluating the performance and efficacy of personalised adaptive learning platforms based on deep learning architectures in classifying learners’ learning styles.