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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Confused student EEG brainwave data. https://www.kaggle.com/datasets/wanghaohan/confused-eeg?select=EEG_data.csv. Accessed 12 May 2023
Weka. https://www.weka.io/. Accessed 12 May 2023
Alexiou, S., Dritsas, E., Kocsis, O., Moustakas, K., Fakotakis, N.: An approach for personalized continuous glucose prediction with regression trees. In: 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), pp. 1–6. IEEE (2021)
Balamurugan, B., Mullai, M., Soundararajan, S., Selvakanmani, S., Arun, D.: Brain-computer interface for assessment of mental efforts in e-learning using the nonmarkovian queueing model. Comput. Appl. Eng. Educ. 29(2), 394–410 (2021)
Boateng, E.Y., Abaye, D.A.: A review of the logistic regression model with emphasis on medical research. J. Data Anal. Inf. Process. 7(4), 190–207 (2019)
Chakraborty, P., Mittal, P., Gupta, M.S., Yadav, S., Arora, A.: Opinion of students on online education during the COVID-19 pandemic. Hum. Behav. Emerg. Technol. 3(3), 357–365 (2021)
Chen, S., Webb, G.I., Liu, L., Ma, X.: A novel selective naïve bayes algorithm. Knowl.-Based Syst. 192, 105361 (2020)
Daghriri, T., Rustam, F., Aljedaani, W., Bashiri, A.H., Ashraf, I.: Electroencephalogram signals for detecting confused students in online education platforms with probability-based features. Electronics 11(18), 2855 (2022)
He, S., Xu, Y., Zhong, L.: EEG-based confusion recognition using different machine learning methods. In: 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE), pp. 826–831. IEEE (2021)
Hossin, M., Sulaiman, M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manage. Process 5(2), 1 (2015)
Khan, A.R., Khosravi, S., Hussain, S., Ghannam, R., Zoha, A., Imran, M.A.: EXECUTE: exploring eye tracking to support E-learning. In: 2022 IEEE Global Engineering Education Conference (EDUCON), pp. 670–676. IEEE (2022)
Maulana, M.F., Defriani, M.: Logistic model tree and decision tree J48 algorithms for predicting the length of study period. PIKSEL: Penelitian Ilmu Komputer Sistem Embed. Log. 8(1), 39–48 (2020)
Ni, Z., Yuksel, A.C., Ni, X., Mandel, M.I., Xie, L.: Confused or not confused? Disentangling brain activity from EEG data using bidirectional LSTM recurrent neural networks. In: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 241–246 (2017)
Reñosa, C.R.M., Bandala, A.A., Vicerra, R.R.P.: Classification of confusion level using EEG data and artificial neural networks. In: 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pp. 1–6. IEEE (2019)
Tahmassebi, A., Gandomi, A.H., Meyer-Baese, A.: An evolutionary online framework for MOOC performance using EEG data. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2018)
Trigka, M., Dritsas, E., Fidas, C.: A survey on signal processing methods for EEG-based brain computer interface systems. In: Proceedings of the 26th Pan-Hellenic Conference on Informatics, pp. 213–218 (2022)
Xu, T., Wang, J., Zhang, G., Zhang, L., Zhou, Y.: Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG. J. Neural Eng. 20(2), 026018 (2023)
Zhang, Z.: Introduction to machine learning: k-nearest neighbors. Ann. Transl. Med. 4(11) (2016)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-44097-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44096-0
Online ISBN: 978-3-031-44097-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)