An Experimental Study of Learning Behaviour in an ELearning Environment

Khawla Alhasan; Liming Chen; Feng Chen

doi:10.1109/HPCC/SmartCity/DSS.2018.00231

An Experimental Study of Learning Behaviour in an ELearning Environment

Khawla Alhasan, Liming Chen, Feng Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.

Original language	English
Title of host publication	An Experimental Study of Learning Behaviour in an ELearning Environment
Place of Publication	Exeter, United Kingdom
Publisher	IEEE Xplore
Pages	1398-1403
Number of pages	5
ISBN (Electronic)	ISBN: 978-1-5386-6614-2
ISBN (Print)	978-1-5386-6615-9
DOIs	https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00231
Publication status	Published (in print/issue) - 30 Jun 2018
Event	2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) - England, Exeter, United Kingdom Duration: 28 Jun 2018 → 30 Jun 2018 https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8605812

Conference

Conference	2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Country/Territory	United Kingdom
City	Exeter
Period	28/06/18 → 30/06/18
Internet address	https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8605812

Keywords

Electronic learning
Visualization
Brian Modeling
Electroencephalography
Gaze tracking
Monitoring
Tracking
Eye tracking
eLearning
learning style
EEG

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/HPCC/SmartCity/DSS.2018.00231

https://ieeexplore.ieee.org/document/8622969/

Cite this

@inproceedings{c3dcf19c6a474710977b06725fc5efec,

title = "An Experimental Study of Learning Behaviour in an ELearning Environment",

abstract = "To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.",

keywords = "Electronic learning, Visualization, Brian Modeling, Electroencephalography, Gaze tracking, Monitoring, Tracking, Eye tracking, eLearning, learning style, EEG",

author = "Khawla Alhasan and Liming Chen and Feng Chen",

year = "2018",

month = jun,

day = "30",

doi = "10.1109/HPCC/SmartCity/DSS.2018.00231",

language = "English",

isbn = "978-1-5386-6615-9",

pages = "1398--1403",

booktitle = "An Experimental Study of Learning Behaviour in an ELearning Environment",

publisher = "IEEE Xplore",

address = "United States",

note = "2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) ; Conference date: 28-06-2018 Through 30-06-2018",

url = "https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8605812",

}

Alhasan, K, Chen, L & Chen, F 2018, An Experimental Study of Learning Behaviour in an ELearning Environment. in An Experimental Study of Learning Behaviour in an ELearning Environment. IEEE Xplore, Exeter, United Kingdom, pp. 1398-1403, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, 28/06/18. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00231

TY - GEN

T1 - An Experimental Study of Learning Behaviour in an ELearning Environment

AU - Alhasan, Khawla

AU - Chen, Liming

AU - Chen, Feng

PY - 2018/6/30

Y1 - 2018/6/30

N2 - To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.

AB - To achieve an adaptive eLearning system, it is essential to monitor the learner behaviour dynamically to diagnose their learning style. Eye tracking can serve that purpose by investigating the eye gaze movement while engaging in the eLearning environment. In this study, an eye tracking experiment was developed to analyse the pattern of learner behaviour to obtain his learning style as a personalisation aspect in an eLearning system. The electroencephalography EEG Emotive Epoc device was used to disclose learners with more accurate data. In the experiment, we have developed a method to test the hypothesis whether the verbal and visual learning styles reflect actual preferences in an eLearning environment based on Felder and Silverman Learning Style Model. The outcome of this experiment can be used as the starting point for further exhaustive experiments. This paper presents the actual state of our experiment, conclusions, and plans for future development.

KW - Electronic learning

KW - Visualization

KW - Brian Modeling

KW - Electroencephalography

KW - Gaze tracking

KW - Monitoring

KW - Tracking

KW - Eye tracking

KW - eLearning

KW - learning style

KW - EEG

U2 - 10.1109/HPCC/SmartCity/DSS.2018.00231

DO - 10.1109/HPCC/SmartCity/DSS.2018.00231

M3 - Conference contribution

SN - 978-1-5386-6615-9

SP - 1398

EP - 1403

BT - An Experimental Study of Learning Behaviour in an ELearning Environment

PB - IEEE Xplore

CY - Exeter, United Kingdom

T2 - 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Y2 - 28 June 2018 through 30 June 2018

ER -

An Experimental Study of Learning Behaviour in an ELearning Environment

Abstract

Conference

Keywords

UN SDGs

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Cite this