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Wang, S., Gwizdka, J. and Chaovalitwongse, W.A. (2016) Using Wireless EEG Signals to Assess Memory Workload in the n-Back Task. IEEE Transactions on Human-Machine Systems, 46, 424-435.
https://doi.org/10.1109/THMS.2015.2476818

has been cited by the following article:

• TITLE: Tracking Students’ Mental Engagement Using EEG Signals during an Interaction with a Virtual Learning Environment

  AUTHORS: Asma Ben Khedher, Imène Jraidi, Claude Frasson

  KEYWORDS: EEG, Engagement Index, Learners’ Performance, Computer-Based Learning Environments

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.11 No.1, January 18, 2019

  ABSTRACT: Monitoring students’ level of engagement during learning activities is an important challenge in the development of tutoring interventions. In this paper, we explore the feasibility of using electroencephalographic signals (EEG) as a tool to monitor the mental engagement index of novice medicine students during a reasoning process. More precisely, the objectives were first, to track students’ mental engagement evolution in order to investigate whether there were particular sections within the learning environment that aroused the highest engagement level among the students, and, if so, did these sections have an impact on learners’ performance. Experimental analyses showed the same trends in the different resolution phases as well as across the different regions of the environments. However, we noticed a higher engagement index during the treatment identification phase since it aroused more mental effort. Moreover statistically significant effects were found between mental engagement and students’ performance.