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

Fehaid Alqahtani; Stamos Katsigiannis; Naeem Ramzan

doi:10.1109/UCET.2019.8881872

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

Fehaid Alqahtani, Stamos Katsigiannis^*, Naeem Ramzan

^*Corresponding author for this work

School of Computing, Engineering and Physical Sciences

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

10 Citations (Scopus)

226 Downloads (Pure)

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 language	English
Title of host publication	4th International Conference on UK - China Emerging Technologies (UCET)
Publisher	IEEE
Number of pages	4
ISBN (Electronic)	9781728127972, 9781728127965
ISBN (Print)	9781728127989
DOIs	https://doi.org/10.1109/UCET.2019.8881872
Publication status	Published - 24 Oct 2019
Event	International Conference on UK - China Emerging Technologies - University of Glasgow, Glasgow, United Kingdom Duration: 21 Aug 2019 → 22 Aug 2019 Conference number: 4 https://www.gla.ac.uk/events/conferences/ucet2019/

Conference

Conference	International Conference on UK - China Emerging Technologies
Abbreviated title	UCET
Country/Territory	United Kingdom
City	Glasgow
Period	21/08/19 → 22/08/19
Internet address	https://www.gla.ac.uk/events/conferences/ucet2019/

Keywords

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

Access to Document

10.1109/UCET.2019.8881872

2019 07 18 Katsigiannis et al ECGAccepted author manuscript, 372 KB

Cite this

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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{\textquoteright}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",

keywords = "Intelligent Tutoring Systems (ITS), Affective computing, ECG, Physiological signals, Machine learning",

author = "Fehaid Alqahtani and Stamos Katsigiannis and Naeem Ramzan",

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AU - Alqahtani, Fehaid

AU - Katsigiannis, Stamos

AU - Ramzan, Naeem

N1 - Conference code: 4

PY - 2019/10/24

Y1 - 2019/10/24

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

U2 - 10.1109/UCET.2019.8881872

DO - 10.1109/UCET.2019.8881872

M3 - Conference contribution

SN - 9781728127989

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

PB - IEEE

T2 - International Conference on UK - China Emerging Technologies

Y2 - 21 August 2019 through 22 August 2019

ER -

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

Abstract

Conference

Keywords

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