Citation

Manzoor, Shahida Raihan and Mohd Isa, Wan Noorshahida and Dollmat, Khairi Shazwan (2022) A Machine-learning Approach to Learner Style Prediction for E-Learning Students. In: Postgraduate Colloquium December 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Abstract

Covid-19 breakout in 2020 made us realize the importance of e-learning while battling unprecedented global crises (Hasani et al.,2020; Dhawan, 2020). Learning Style (LS) is a key contributor to personalization and has a powerful impact on a learner’s performance as well as information retention in e-learning (İlçin et al.,2018; Lara, Lluna & López, 2019). Existing work relevant to LS prediction include- A number of research on the LS prediction but most focusing solely on the Felder and Silverman model or Kolb’s model. Very little research has been done focusing on VARK model (Visual, Auditory, & Kinesthetic, Reader/Writer) Model variables selected either from questionnaires, behavioral patterns, test scores, web usage mining, LMS logs. Not many have used a mixed method for their models. Models utilized machine learning (ML) algorithms like- Naïve Bayes, Fuzzy Logic, Model decision tree, Artificial Neural Network (ANN) for prediction but there are little work done utilizing Support Vector Machine(SVM).

Item Type:	Conference or Workshop Item (Poster)
Uncontrolled Keywords:	Machine Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	19 Dec 2022 09:09
Last Modified:	19 Dec 2022 09:09
URII:	http://shdl.mmu.edu.my/id/eprint/10931

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