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Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model

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Abstract

A learning style that focuses on individual learning is one of the most important aspects of any learning environment. Each learner has a unique manner of understanding, retaining, processing, and interpreting new information based on their learning styles. The ability of an e-learning system to automatically determine a student's learning style has become more essential. For learning events, the evolution of e-learning platforms provides students with higher opportunities online. In this paper, we proposed a Convolutional Neural Network-based Levy Flight Distribution (CNN-LFD) algorithm for learning style prediction. An adaptive e-learning system is divided into two sections: automatic learning style prediction and classification based on the number of learning styles included. Initially, the student logs in with their user ID, and the data is saved in the database. The features such as questionnaire score, login credentials (session ID, learner ID, and course ID), and login time (location, session ID) are extracted along with the sequence of the user's learning behavior. After that, the CNN-LFD algorithm predicts the learning styles of the learners namely Active/reflective, Sensing/intuitive, visual/verbal, sequential/global based on the extracted features. The dataset information are gathered from a Massive Open Online Course (MOOC), and the proposed model is built in JAVA software. The experimental results demonstrate higher classification accuracy during learning style prediction. The proposed CNN-LFD algorithm accomplishes 97.09% accuracy, 94.76% specificity, 92.12% sensitivity, and 97.56%, precision values than other methods.

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  1. Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi Arabia

    Sami Alshmrany

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Alshmrany, S. Adaptive learning style prediction in e-learning environment using levy flight distribution based CNN model. Cluster Comput 25, 523–536 (2022). https://doi.org/10.1007/s10586-021-03403-3

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