research-article

Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style

Authors:

Yeimy Paola Valencia Usme,

Galia WeidlAuthors Info & Claims

ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education

Pages 73 - 82

https://doi.org/10.1145/3593663.3593685

Published: 19 June 2023 Publication History

Abstract

In this paper, we propose a classification model constructed with an algorithm based on Object-Oriented Bayesian networks (OOBN) to determine the learning style of a student. For this, the Felder-Silverman Learning Style Model (FSLSM) is used, which is based on the Index of Learning Style (ILS) questionnaire. The idea is to use the answers to the questionnaire as provided by a student - as the input of the OOBN model to classify the learning style of this student. The classifications made by the OOBN model are validated with the full questionnaire with 44 questions as well as a short version with only 20 questions. The results of the OOBN classification with 44 individually answered questions represent the ground truth to compare the classifier performance in case a reduced set of questions is used. The OOBN with 20 questions suggest that the approach is classifying the students into the correct learning style dimensions in most cases. This indicates a possible to use BN within an Adaptive Learning System (ALS) like HASKI.

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Cited By

Masegosa ACabañas RMaldonado AMorales M(2024)Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming ExercisesEducation Sciences10.3390/educsci1403025014:3(250)Online publication date: 28-Feb-2024
https://doi.org/10.3390/educsci14030250

Index Terms

Learning Style Classification by Using Bayesian Networks Based on the Index of Learning Style
1. Software and its engineering

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ECSEE '23: Proceedings of the 5th European Conference on Software Engineering Education

June 2023

264 pages

ISBN:9781450399562

DOI:10.1145/3593663

Editor:
Jürgen Mottok
OTH Regensburg

Copyright © 2023 ACM.

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Published: 19 June 2023

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German Federal Ministry of Education and Research (BMBF)

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ECSEE 2023

ECSEE 2023: European Conference on Software Engineering Education

June 19 - 21, 2023

Seeon/Bavaria, Germany

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Overall Acceptance Rate 11 of 16 submissions, 69%

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Cited By

Masegosa ACabañas RMaldonado AMorales M(2024)Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming ExercisesEducation Sciences10.3390/educsci1403025014:3(250)Online publication date: 28-Feb-2024
https://doi.org/10.3390/educsci14030250

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