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SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

Aditya Khamparia, Babita Pandey
Copyright: © 2018 |Volume: 13 |Issue: 2 |Pages: 14
ISSN: 1548-1093|EISSN: 1548-1107|EISBN13: 9781522542490|DOI: 10.4018/IJWLTT.2018040103
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MLA

Khamparia, Aditya, and Babita Pandey. "SVM and PCA Based Learning Feature Classification Approaches for E-Learning System." IJWLTT vol.13, no.2 2018: pp.32-45. http://doi.org/10.4018/IJWLTT.2018040103

APA

Khamparia, A. & Pandey, B. (2018). SVM and PCA Based Learning Feature Classification Approaches for E-Learning System. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 13(2), 32-45. http://doi.org/10.4018/IJWLTT.2018040103

Chicago

Khamparia, Aditya, and Babita Pandey. "SVM and PCA Based Learning Feature Classification Approaches for E-Learning System," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT) 13, no.2: 32-45. http://doi.org/10.4018/IJWLTT.2018040103

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Abstract

E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead of static content, according to their skills, needs and preferences. In this article, the authors have classified eight different types of student learning attributes based on National Centre for Biotechnical Information (NCBI) e-learning database. The eight types of attributes are Anxiety (A), Personality (P), Learning style (L), Cognitive style (C), Grades from previous sem (GP), Motivation (M), Study level (SL) and Student prior knowledge (SPK). In this article the authors have proposed an approach which uses principal components of student learning attributes and have later independently classified these attributes using feed forward neural network (NN) and Least Square –Support Vector Machine (LS-SVM).

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