IDEAS home Printed from https://ideas.repec.org/a/igg/jwltt0/v13y2018i2p32-45.html
   My bibliography  Save this article

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

Author

Listed:
  • Aditya Khamparia

    (Lovely Professional University, Jalandhar, India)

  • Babita Pandey

    (Lovely Professional University, Jalandhar, India)

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).

Suggested Citation

  • Aditya Khamparia & Babita Pandey, 2018. "SVM and PCA Based Learning Feature Classification Approaches for E-Learning System," International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), IGI Global, vol. 13(2), pages 32-45, April.
    • Handle: RePEc:igg:jwltt0:v:13:y:2018:i:2:p:32-45
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWLTT.2018040103
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jie Hu & Yi Peng & Xueliang Chen & Hangyan Yu, 2021. "Differentiating the learning styles of college students in different disciplines in a college English blended learning setting," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-26, May.
    2. Ilie Gligorea & Muhammad Usman Yaseen & Marius Cioca & Hortensia Gorski & Romana Oancea, 2022. "An Interpretable Framework for an Efficient Analysis of Students’ Academic Performance," Sustainability, MDPI, vol. 14(14), pages 1-21, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jwltt0:v:13:y:2018:i:2:p:32-45. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.