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Using wearable physiological sensors for affect-aware Intelligent Tutoring Systems

Alqahtani, Fehaid; Katsigiannis, Stamos; Ramzan, Naeem

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Fehaid Alqahtani

Naeem Ramzan


Intelligent Tutoring Systems (ITS) have shown great potential in enhancing the learning process by being able to adapt to the learner’s knowledge level, abilities, and difficulties. An aspect that can affect the learning process but is not taken into consideration by traditional ITS is the affective state of the learner. In this work, we propose the use of physiological signals and machine learning for the task of detecting a learner’s affective state during test taking. To this end, wearable physiological sensors were used to record electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG) signals from 27 individuals while participating in a computerised English language test. Features extracted from the acquired signals were used in order to train machine learning models for the prediction of the self-reported difficulty level of the test’s questions, as well as for the prediction of whether the questions would be answered correctly. Supervised classification experiments showed that there is a relation between the acquired signals and the examined tasks, reaching a classification F1-score of 74.21% for the prediction of the self-reported question difficulty level, and a classification F1-score of 59.14% for predicting whether a question was answered correctly. The acquired results demonstrate the potential of the examined approach for enhancing ITS with information relating to the affective state of the learners.


Alqahtani, F., Katsigiannis, S., & Ramzan, N. (2021). Using wearable physiological sensors for affect-aware Intelligent Tutoring Systems. IEEE Sensors Journal, 21(3), 3366-3378.

Journal Article Type Article
Acceptance Date Sep 8, 2020
Online Publication Date Sep 14, 2020
Publication Date 2021-02
Deposit Date Sep 11, 2020
Publicly Available Date Jan 20, 2021
Journal IEEE Sensors Journal
Print ISSN 1530-437X
Electronic ISSN 1558-1748,2379-9153
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 21
Issue 3
Pages 3366-3378


Accepted Journal Article (700 Kb)

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