Cognitive Learning Style Detection in e-Learning Environments using Artificial Neural Network
DOI:
https://doi.org/10.3991/ijet.v17i17.30243Keywords:
learning style approach, FSLM, artificial neural network, cognitive capacityAbstract
COVID-19 pandemic has impacted all aspects of our lives including learning. With the particular growth of e-learning, teaching approaches are being implemented at a distance on online platforms due to this pandemic. In this context, to make student involved throughout the online course, it is recommended to create an efficient platform similar to the traditional learning mode. In this study, we aims to improve learning style detection process by exploring additional such as cognitive traits. In fact, we have proposed novel approach based on Artificial neural network that classify students according to their level of cognitive learning styles in real-time. The proposed automated approach will certainly provide tutors with exhaustive information that helps them in achieving an improved and innovative online learning method. The results obtained are quite interesting and demonstrate the relevance of our solution.
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Copyright (c) 2022 Samia Rami, Samir Bennani, Mohammed Khalidi Idrissi
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