文章基本信息

标题：Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System
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作者：Sucheta V Kolekar ; Radhika M Pai ; Manohara Pai M M 等
期刊名称：International Journal of Emerging Technologies in Learning (iJET)
印刷版ISSN：1863-0383
出版年度：2017
卷号：12
期号：6
页码：31-51
语种：English
出版社：Kassel University Press
其他摘要：The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.
关键词：Felder-Silverman Learning Style Model; Fuzzy C Means; Gravitational Search Algorithm; Back Propagation Neural Network Model; Jaccard Index; Xie-Beni Index