Abstract
In cognitive science, the term confusion is used to capture the decline in learners’ cognitive ability, which affects their ability to think, solve a problem, learn and understand. Unlike classroom education, in online e-learning contexts (e.g., in Massive Open Online Courses (MOOC)), confusion hinders the smooth evolution of the learning process from the learners’ side, as the educator can’t immediately interact with the students to restore cognitive equilibrium. This paper presents a Machine Learning (ML) based approach by comparing several classifiers that were trained and tested exploiting Electroencephalogram (EEG) data (namely, band power, attention and mediation features) acquired by the MindSet device to efficiently distinguish “Confused” from “Not-Confused” subjects. In particular, J48 was the dominant model reaching an optimal performance with accuracy, precision and recall equal to 99.9% and an Area Under the Curve (AUC) of 100%.
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Acknowledgements
This research was funded by the European Union and Greece (Partnership Agreement for the Development Framework 2014–2020) under the Regional Operational Programme Ionian Islands 2014–2020, project title: “Indirect costs for project “Smart digital applications and tools for the effective promotion and enhancement of the Ionian Islands bio-diversity””, project number: 5034557.
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Trigka, M., Dritsas, E., Mylonas, P. (2023). Mental Confusion Prediction in E-Learning Contexts with EEG and Machine Learning. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 783. Springer, Cham. https://doi.org/10.1007/978-3-031-44097-7_21
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