Personalization of E-Learning Systems: Determination of Most Preferred Learning Style Using Conjoint Analysis

Authors

  • J. Saul Nicholas Research Scholar, Department of Computer Science and Applications, SCSVMV University, Enathur, Kanchipuram, Tamil Nadu, India
  • F. Sagayaraj Francis Professor, Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.3.1889

Keywords:

Adaptive Systems, Personalized Learning, Learning Styles, Adaptive Framework, Conjoint Analysis, FSLSM, Time Factor, Frequency Factor

Abstract

Identifying user preferences is a very important activity before offering a suggestion or a product. E-learning systems also follow suit in identifying the user preferences of learning style before offering the e-learning contents. There are several methods discussed in the literature for identifying the user preferences for e-learning contents. This paper presents a new method for the same purpose. The core of the new method is Conjoint Analysis, which is based on the type of the contents, preferred volume for each type of content and the ranking for the various combinations of the contents and their preferred volumes. The outcome of this method is the most preferred learning style of an individual learner.

References

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D. Raghavarao, B. James Wiley and P. Chitturi, Choice-Based Conjoint Analysis, Models and Designs, Taylor and Francis Group, 2010.

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Published

05-11-2018

How to Cite

Saul Nicholas, J., & Sagayaraj Francis, F. (2018). Personalization of E-Learning Systems: Determination of Most Preferred Learning Style Using Conjoint Analysis. Asian Journal of Computer Science and Technology, 7(3), 91–95. https://doi.org/10.51983/ajcst-2018.7.3.1889

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