Universiti Teknologi Malaysia Institutional Repository

Modeling learning styles based on the student behavior in hypermedia learning system using neural network

Ahmad, Nor Bahiah and Shamsuddin, Siti Mariyam (2006) Modeling learning styles based on the student behavior in hypermedia learning system using neural network. In: Proc. Postgraduate Annual Research Seminar 2006 (PARS 2006) , 2006, UTM.

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Official URL: http://comp.utm.my/pars/files/2013/04/Modeling-Lea...

Abstract

Identification of the characteristics and learning style of a student while learning on-line is one of the important features of adaptation in adaptive hypermedia learning system. Most systems asks learners to complete questionnaire to identify the student’s learning style. However, the problem with these method is the time students spend answering questions and the accuracy of the results obtained. If questionnaires are too long, students tend to choose answers arbitrarily instead of thinking about the result The learner’s learning style can be observed through his web behavior which concerned on how user navigate, how user use the link and path provided, choose the type of learning material and the usage of the tool provided. This research describes the classification of students learning style based on Felder Silverman learning dimension. Four learning dimension has been classified using backpropagation neural network. The algorithm has been run on hundred training data and fifty testing data using sigmoid transfer function. The result shows that neural network is able to classify the user according to the dimension with satisfying result.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:learning style, neural network, backpropagation, classification, hypermedia learning system
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System (Formerly known)
ID Code:25309
Deposited By: Liza Porijo
Deposited On:17 May 2012 04:32
Last Modified:10 Jun 2014 03:58

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