Prediction of Learner's Profile based on Learning Styles in Adaptive E-learning System

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.

[1]  Elvira Popescu,et al.  Using Artificial Neural Networks to Identify Learning Styles , 2015, AIED.

[2]  Manal Abdullah,et al.  Learning Style Classification Based on Student's Behavior in Moodle Learning Management System , 2015 .

[3]  Katerina Kostolányová,et al.  Analysis of Learning Styles for Adaptive E-Learning , 2011, ICDIPC.

[4]  Gozde Bozdagi Akar,et al.  Automatic detection of learning styles for an e-learning system , 2009, Comput. Educ..

[5]  Chih-Ping Chu,et al.  A learning style classification mechanism for e-learning , 2009, Comput. Educ..

[6]  T. Chellatamilan,et al.  Effect of mining educational data to improve adaptation of learning in e-learning system , 2011 .

[7]  Atta Badii,et al.  STATE OF THE ART OF LEARNING STYLES-BASED ADAPTIVE EDUCATIONAL HYPERMEDIA SYSTEMS (LS-BAEHSS) , 2014 .

[8]  Manal Abdullah,et al.  The Impact of Learning Styles on Learner's Performance in E-Learning Environment , 2015 .

[9]  L JEGATHA DEBORAH,et al.  Fuzzy-logic based learning style prediction in e-learning using web interface information , 2015, Sadhana.

[10]  Analía Amandi,et al.  Automatic detection of learning styles: state of the art , 2014, Artificial Intelligence Review.

[11]  Quang Hung Do,et al.  A Hybrid Gravitational Search Algorithm and Back-Propagation for Training Feedforward Neural Networks , 2014, KSE.

[12]  S. Karsoliya,et al.  Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture , 2012 .

[13]  Huong May Truong.  Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities , 2016, Comput. Hum. Behav..

[14]  David McLean,et al.  Profiling Student Learning Styles with Multilayer Perceptron Neural Networks , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[15]  Agbonifo,et al.  Fuzzy C-Means Clustering Model for Identification of Students ’ Learning Preferences in Online Environment , 2014 .

[16]  Pawan Lingras,et al.  Automatic Determination of Learning Styles , .

[17]  Mofreh A. Hogo,et al.  Evaluation of E-Learners Behaviour using Different Fuzzy Clustering Models: A Comparative Study , 2010, ArXiv.

[18]  Mazidah Puteh,et al.  An overview of Gravitational Search Algorithm utilization in optimization problems , 2013, 2013 IEEE 3rd International Conference on System Engineering and Technology.

[19]  Danuta Zakrzewska,et al.  Using Clustering Technique for Students' Grouping in Intelligent E-Learning Systems , 2008, USAB.

[20]  Urszula Markowska-Kaczmar,et al.  Intelligent Techniques in Personalization of Learning in e-Learning Systems , 2010, Computational Intelligence for Technology Enhanced Learning.