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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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)
Number of pages4
ISBN (Electronic)9781728127972, 9781728127965
ISBN (Print)9781728127989
Publication statusPublished - 24 Oct 2019
EventInternational Conference on UK - China Emerging Technologies - University of Glasgow, Glasgow, United Kingdom
Duration: 21 Aug 201922 Aug 2019
Conference number: 4


ConferenceInternational Conference on UK - China Emerging Technologies
Abbreviated titleUCET
CountryUnited Kingdom
Internet address



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

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