A learning style classification mechanism for e-learning

Yi Chun Chang, Wen Yan Kao, Chih Ping Chu, Chiung Hui Chiu

Research output: Contribution to journalArticle

96 Citations (Scopus)

Abstract

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.

Original languageEnglish
Pages (from-to)273-285
Number of pages13
JournalComputers and Education
Volume53
Issue number2
DOIs
Publication statusPublished - 2009 Sep 1

Fingerprint

electronic learning
Students
learning
student
learning environment
open learning
Teaching
learning performance
Genetic algorithms
learning strategy
elementary school
demand
management

Keywords

  • Adaptive learning
  • E-learning
  • Genetic algorithm (GA)
  • Learning style
  • k-Nearest neighbor classification

ASJC Scopus subject areas

  • Computer Science(all)
  • Education

Cite this

A learning style classification mechanism for e-learning. / Chang, Yi Chun; Kao, Wen Yan; Chu, Chih Ping; Chiu, Chiung Hui.

In: Computers and Education, Vol. 53, No. 2, 01.09.2009, p. 273-285.

Research output: Contribution to journalArticle

Chang, Yi Chun ; Kao, Wen Yan ; Chu, Chih Ping ; Chiu, Chiung Hui. / A learning style classification mechanism for e-learning. In: Computers and Education. 2009 ; Vol. 53, No. 2. pp. 273-285.
@article{bf33fe3f13f74fb4ac91bbb60b9c8639,
title = "A learning style classification mechanism for e-learning",
abstract = "With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.",
keywords = "Adaptive learning, E-learning, Genetic algorithm (GA), Learning style, k-Nearest neighbor classification",
author = "Chang, {Yi Chun} and Kao, {Wen Yan} and Chu, {Chih Ping} and Chiu, {Chiung Hui}",
year = "2009",
month = "9",
day = "1",
doi = "10.1016/j.compedu.2009.02.008",
language = "English",
volume = "53",
pages = "273--285",
journal = "Computers and Education",
issn = "0360-1315",
publisher = "Elsevier Limited",
number = "2",

}

TY - JOUR

T1 - A learning style classification mechanism for e-learning

AU - Chang, Yi Chun

AU - Kao, Wen Yan

AU - Chu, Chih Ping

AU - Chiu, Chiung Hui

PY - 2009/9/1

Y1 - 2009/9/1

N2 - With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.

AB - With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.

KW - Adaptive learning

KW - E-learning

KW - Genetic algorithm (GA)

KW - Learning style

KW - k-Nearest neighbor classification

UR - http://www.scopus.com/inward/record.url?scp=67349228542&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67349228542&partnerID=8YFLogxK

U2 - 10.1016/j.compedu.2009.02.008

DO - 10.1016/j.compedu.2009.02.008

M3 - Article

AN - SCOPUS:67349228542

VL - 53

SP - 273

EP - 285

JO - Computers and Education

JF - Computers and Education

SN - 0360-1315

IS - 2

ER -