ECG-based affective computing for difficulty level prediction in intelligent tutoring systems

Fehaid Alqahtani, Stamos Katsigiannis*, Naeem Ramzan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test’s questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22% for the prediction of the self-assessed difficulty level of the questions
Original languageEnglish
Title of host publication4th International Conference on UK - China Emerging Technologies (UCET)
PublisherIEEE
Number of pages4
Publication statusAccepted/In press - 18 Jul 2019
EventInternational Conference on UK - China Emerging Technologies - University of Glasgow, Glasgow, United Kingdom
Duration: 21 Aug 201922 Aug 2019
Conference number: 4
https://www.gla.ac.uk/events/conferences/ucet2019/

Conference

ConferenceInternational Conference on UK - China Emerging Technologies
Abbreviated titleUCET
CountryUnited Kingdom
CityGlasgow
Period21/08/1922/08/19
Internet address

Fingerprint

Intelligent systems
Electrocardiography
Experiments

Keywords

  • Intelligent Tutoring Systems (ITS)
  • Affective computing
  • ECG
  • Physiological signals
  • Machine learning

Cite this

Alqahtani, F., Katsigiannis, S., & Ramzan, N. (Accepted/In press). ECG-based affective computing for difficulty level prediction in intelligent tutoring systems. In 4th International Conference on UK - China Emerging Technologies (UCET) IEEE.
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title = "ECG-based affective computing for difficulty level prediction in intelligent tutoring systems",
abstract = "Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test’s questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22{\%} for the prediction of the self-assessed difficulty level of the questions",
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Alqahtani, F, Katsigiannis, S & Ramzan, N 2019, ECG-based affective computing for difficulty level prediction in intelligent tutoring systems. in 4th International Conference on UK - China Emerging Technologies (UCET). IEEE, International Conference on UK - China Emerging Technologies, Glasgow, United Kingdom, 21/08/19.

ECG-based affective computing for difficulty level prediction in intelligent tutoring systems. / Alqahtani, Fehaid; Katsigiannis, Stamos; Ramzan, Naeem.

4th International Conference on UK - China Emerging Technologies (UCET). IEEE, 2019.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - ECG-based affective computing for difficulty level prediction in intelligent tutoring systems

AU - Alqahtani, Fehaid

AU - Katsigiannis, Stamos

AU - Ramzan, Naeem

PY - 2019/7/18

Y1 - 2019/7/18

N2 - Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test’s questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22% for the prediction of the self-assessed difficulty level of the questions

AB - Intelligent tutoring Systems (ITS) have emerged as an attractive solution for providing personalised learning experiences on a large scale. Traditional ITS are able to adapt the learning process according to the capabilities and needs of their users, but lack the capability to adapt to their affective/emotional state. In this work, we examine the use of electrocardiography (ECG) signals for detecting the affective state of ITS users. Features, extracted from ECG signals acquired while users undertook a computerised English language test, were used for the prediction of the self-reported difficulty level of the test’s questions. Supervised classification experiments demonstrated the potential of this approach, achieving a classification F1-score of 61.22% for the prediction of the self-assessed difficulty level of the questions

KW - Intelligent Tutoring Systems (ITS)

KW - Affective computing

KW - ECG

KW - Physiological signals

KW - Machine learning

M3 - Conference contribution

BT - 4th International Conference on UK - China Emerging Technologies (UCET)

PB - IEEE

ER -

Alqahtani F, Katsigiannis S, Ramzan N. ECG-based affective computing for difficulty level prediction in intelligent tutoring systems. In 4th International Conference on UK - China Emerging Technologies (UCET). IEEE. 2019