Determining Learning Style Preferences of Learners

Sushil Shrestha (Digital Learning Research Lab, Department of Computer Science and Engineering, Kathmandu University, Nepal)
Manish Pokharel (Digital Learning Research Lab, Department of Computer Science and Engineering, Kathmandu University, Nepal)

Abstract


The use of Information and Communication Technology (ICT) in education has been rapidly growing in recent years which has converted conventional classrooms teaching environments into online learning (OL) environment. Online learning system is gaining popular and widely accepted in the world due to the current pandemic due to COVID 19. This has created an opportunity to take online classes through several online learning platforms. This research was also done during pandemic. The data were collected from one of the undergraduate courses where there were 108 learners. The objective of the study is to determine the learning style preferences based on the learner’s interactions data. one of the popular and widely used learning style model called Felder Silverman Learning Style Model (FSLSM) was implemented in this study to determine the learning preferences. The learners were classified according to the two dimensions i.e., input and processing of FSLSM. Further, two popular treebased classifier such as decision tree and random forest were implemented. Decision tree had a better performance in terms of accuracy than random forest. This type of research is very much beneficial to the instructors, learners and researchers and administrators working in the field of online learning.

 

 

 
A Correction to this article was published on 25 January 2022.
This article has been updated






Keywords


Online learning;Moodle;FSLSM;Data mining;Decision tree;Random forest

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References


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DOI: https://doi.org/10.30564/jcsr.v3i1.2761

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