Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems

Arevalillo-Herráez, Miguel; Katsigiannis, Stamos; Alqahtani, Fehaid; Arnau-González, Pablo

Abstract

Accurately assessing task difficulty is a critical aspect to achieve adaptation in computer-based educational systems. In real-world scenarios, task difficulty estimation can be personalised for individuals by leveraging Item Response Theory (IRT) to analyse the collective performance of a group of students across various tasks. Additionally, recent studies have revealed the potential of inferring task difficulty through the analysis of physiological signals, such as electrocardiography (ECG). In this paper, we propose a novel hybrid approach that combines both methodologies to enhance task difficulty estimates, surpassing the individual performance of each method. The availability of non-intrusive techniques for capturing heart rate adds further value to the proposal, facilitating its potential integration into future computer-based educational systems. Experimental results on a dataset captured during two computerised English tests show that our proposed hybrid approach outperforms each individual method for the task of difficulty estimation.

Citation

Arevalillo-Herráez, M., Katsigiannis, S., Alqahtani, F., & Arnau-González, P. (2023). Fusing ECG signals and IRT models for task difficulty prediction in computerised educational systems. Knowledge-Based Systems, 280, Article 111052. https://doi.org/10.1016/j.knosys.2023.111052

• Journal Article Type: Article
• Acceptance Date: Sep 30, 2023
• Online Publication Date: Oct 7, 2023
• Publication Date: Nov 25, 2023
• Deposit Date: Oct 11, 2023
• Publicly Available Date: Oct 18, 2023
• Journal: Knowledge-Based Systems
• Print ISSN: 0950-7051
• Electronic ISSN: 1872-7409
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 280
• Article Number: 111052
• DOI: https://doi.org/10.1016/j.knosys.2023.111052
• Public URL: https://durham-repository.worktribe.com/output/1758603