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

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Title: SVM and PCA Based Learning Feature Classification Approaches for E-Learning System
Language: English
Authors: Khamparia, Aditya, Pandey, Babita
Source: International Journal of Web-Based Learning and Teaching Technologies. 2018 13(2):32-45.
Availability: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Peer Reviewed: Y
Page Count: 14
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Descriptors: Classification, Electronic Learning, Online Courses, Anxiety, Personality, Cognitive Style, Learning Motivation, Prior Learning, Factor Analysis, Least Squares Statistics, Reliability, Accuracy, Preferences, Academic Ability, Student Needs, Foreign Countries, Computer Attitudes, Attitude Measures, Statistical Analysis
Geographic Terms: Japan
Assessment and Survey Identifiers: Computer Anxiety Scale
DOI: 10.4018/IJWLTT.2018040103
ISSN: 1548-1093
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 ofstatic 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).
Abstractor: As Provided
Number of References: 48
Entry Date: 2018
Accession Number: EJ1170716
Database: ERIC
FullText Text:
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  Data: SVM and PCA Based Learning Feature Classification Approaches for E-Learning System
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  Data: IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
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  Data: 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 ofstatic 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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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1170716
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