On the use of ECG and EMG signals for question difficulty level prediction in the context of Intelligent Tutoring Systems

Fehaid Alqahtani, Stamos Katsigiannis, Naeem Ramzan

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

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Abstract

A fundamental drawback of traditional Intelligent Tutoring Systems (ITS) is that, unlike human tutors, they are not able to understand the emotional state of their users and adapt the learning process accordingly. This work explores the potential use of affective computing techniques for providing an affect detection mechanism for ITS. Electrocardiography (ECG) and electromyography (EMG) signals were recorded from 45 individuals that undertook a computerised English language test and provided feedback on the difficulty of the test's questions. Features extracted from the ECG and EMG signals were then used in order to train machine learning models for the task of predicting the self-perceived difficulty level of the questions. The conducted supervised classification experiments provided promising results for the suitability of this approach for enhancing ITS with information relating to the affective state of the learners, reaching an average classification F1-score of 75.49% for the personalised single-participant models and a classification F1-score of 64.10% for the global models.
Original languageEnglish
Title of host publication2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
PublisherIEEE
Pages392-396
Number of pages6
ISBN (Electronic)9781728146171
ISBN (Print)9781728146188
DOIs
Publication statusPublished - 31 Oct 2019
EventThe 19th IEEE International Conference on Bioinformatics and Bioengineering - Royal Olympic Hotel, Athens, Greece
Duration: 28 Oct 201930 Oct 2019
https://bibe2019.ics.forth.gr/ (Conference website.)

Publication series

NameIEEE Conference Proceedings
PublisherIEEE
ISSN (Print)2159-5410
ISSN (Electronic)2471-7819

Conference

ConferenceThe 19th IEEE International Conference on Bioinformatics and Bioengineering
Abbreviated titleBIBE2019
CountryGreece
CityAthens
Period28/10/1930/10/19
Internet address

Keywords

  • Intelligent tutoring systems (ITS)
  • Affective computing
  • ECG
  • EMG
  • Physiological signals
  • Machine learning

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    Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2019). On the use of ECG and EMG signals for question difficulty level prediction in the context of Intelligent Tutoring Systems. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 392-396). (IEEE Conference Proceedings). IEEE. https://doi.org/10.1109/BIBE.2019.00077