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Classification Accuracy in Cognitive Load for Users Preference in Web based Learning

by L. Jayasimman, E. Geroge Dharma Prakash Raj
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 16
Year of Publication: 2012
Authors: L. Jayasimman, E. Geroge Dharma Prakash Raj
10.5120/8653-2559

L. Jayasimman, E. Geroge Dharma Prakash Raj . Classification Accuracy in Cognitive Load for Users Preference in Web based Learning. International Journal of Computer Applications. 54, 16 ( September 2012), 37-41. DOI=10.5120/8653-2559

@article{ 10.5120/8653-2559,
author = { L. Jayasimman, E. Geroge Dharma Prakash Raj },
title = { Classification Accuracy in Cognitive Load for Users Preference in Web based Learning },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 16 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number16/8653-2559/ },
doi = { 10.5120/8653-2559 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:55:53.125805+05:30
%A L. Jayasimman
%A E. Geroge Dharma Prakash Raj
%T Classification Accuracy in Cognitive Load for Users Preference in Web based Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 16
%P 37-41
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With increasing popularity of web based learning, it is required to design the web layout to reduce cognitive load. Cognitive load theory is widely used to predict the effectiveness of the web based and multimedia learning. The cognitive load induced by instructional and multimedia modes are measured by indirect or subjective methods. Questionnaires are one common form of measuring cognitive load indirectly. In this paper, a questionnaire is prepared to identify the cognitive load of the student and his website preferences in a web learning environment. The cognitive attributes are used as the training input for the Naïve Bayes, Classification Regression Tree(CART), Random Forest and Random Tree for classification. Based on the response of the user, areas for improvement in layout of the web learning system are identified.

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Index Terms

Computer Science
Information Sciences

Keywords

User interface design Cognitive approach online learning decision tree induction Introduction

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