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

DOI: 10.4236/jilsa.2019.111001, PP. 1-14

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

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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.

References

[1]  Jraidi, I., Chaouachi, M. and Frasson, C. (2013) A Dynamic Multimodal Approach for Assessing Learners’ Interaction Experience. 15th International Conference on Multimodal Interaction, Sydney, 9-13 December 2013, 271-278.
https://doi.org/10.1145/2522848.2522896
[2]  Ben Khedher, A., Jraidi, I. and Frasson, C. (2018) Static and Dynamic Eye Movement Metrics for Students’ Performance Assessment. Smart Learning Environments, 5, 14.
https://doi.org/10.1186/s40561-018-0065-y
[3]  Ben Khedher, A., Jraidi, I. and Frasson, C. (2017) Assessing Learners’ Reasoning Using Eye Tracking and a Sequence Alignment Method. International Conference on Intelligent Computing, Liverpool, 7-10 August 2017, 47-57.
https://doi.org/10.1007/978-3-319-63312-1_5
[4]  Roach, B.J. and Mathalon, D.H. (2008) Event-Related EEG Time-Frequency Analysis: An Overview of Measures and an Analysis of Early Gamma Band Phase Locking in Schizophrenia. Schizophrenia Bulletin, 34, 907-926.
https://doi.org/10.1093/schbul/sbn093
[5]  Ben Hamida, S., Penzel, T. and Ahmed, B. (2015) EEG Time and Frequency Domain Analysis of Primary Insomnia. 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 25-29 August 2015, 6206-6209.
[6]  Loo, S.K., Lenartowicz, A. and Makeig, S. (2016) Use of EEG Biomarkers in Child Psychiatry Research: Current State and Future Directions. Journal of Child Psychology and Psychiatry, 57, 4-17.
https://doi.org/10.1111/jcpp.12435
[7]  Soleymani, M., Asghari-Esfeden, S., Fu, Y. and Pantic, M. (2016) Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection. IEEE Transactions on Affective Computing, 7, 17-28.
https://doi.org/10.1109/TAFFC.2015.2436926
[8]  Kim, J., Seo, J. and Laine, T.H. (2018) Detecting Boredom from Eye Gaze and EEG. Biomedical Signal Processing and Control, 46, 302-313.
https://doi.org/10.1016/j.bspc.2018.05.034
[9]  Zhuang, N., Zeng, Y., Tong, L., Zhang, C., Zhang, H. and Yan, B. (2017) Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain. BioMed Research International, 2017, Article ID: 8317357.
https://doi.org/10.1155/2017/8317357
[10]  Wang, X.-W., Nie, D. and Lu, B.-L. (2014) Emotional State Classification from EEG Data Using Machine Learning Approach. Neurocomputing, 129, 94-106.
https://doi.org/10.1016/j.neucom.2013.06.046
[11]  Jraidi, I., Chaouachi, M. and Frasson, C. (2014) A Hierarchical Probabilistic Framework for Recognizing Learners’ Interaction Experience Trends and Emotions. Advances in Human-Computer Interaction, 2014, Article ID: 632630.
https://doi.org/10.1155/2014/632630
[12]  Lujan-Moreno, G.A., Atkinson, R.K. and Runger, G. (2016) EEG-Based User Performance Prediction Using Random Forest in a Dynamic Learning Environment. Intelligent Tutoring Systems: Structure, Applications and Challenges, 105-128.
[13]  Jraidi, I., and Frasson, C. (2010) Subliminally Enhancing Self-Esteem: Impact on Learner Performance and Affective State. Intelligent Tutoring Systems, 11-20.
https://doi.org/10.1007/978-3-642-13437-1_2
[14]  Van der Hiele, K., et al. (2007) EEG Correlates in the Spectrum of Cognitive Decline. Clinical Neurophysiology, 118, 1931-1939.
https://doi.org/10.1016/j.clinph.2007.05.070
[15]  Jraidi, I., Chalfoun, P. and Frasson, C. (2012) Implicit Strategies for Intelligent Tutoring Systems. Intelligent Tutoring Systems, 1-10.
[16]  Ben Khedher, A., Jraidi, I. and Frasson, C. (2018) Exploring Students’ Eye Movements to Assess Learning Performance in a Serious Game. EdMedia + Innovate Learning, 394-401.
[17]  Aricò, P., Borghini, G., Di Flumeri, G., Colosimo, A., Pozzi, S. and Babiloni, F. (2016) A Passive Brain-Computer Interface Application for the Mental Workload Assessment on Professional Air Traffic Controllers during Realistic Air Traffic Control Tasks. Progress in Brain Research, 228, 295-328.
https://doi.org/10.1016/bs.pbr.2016.04.021
[18]  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
[19]  Keith, J.R., Rapgay, L., Theodore, D., Schwartz, J.M. and Ross, J.L. (2015) An Assessment of an Automated EEG Biofeedback System for Attention Deficits in a Substance Use Disorders Residential Treatment Setting. Psychology of Addictive Behaviors, 29, 17-25.
https://doi.org/10.1037/adb0000016
[20]  Jraidi, I. and Frasson, C. (2013) Student’s Uncertainty Modeling through a Multimodal Sensor-Based Approach. Journal of Educational Technology & Society, 16, 219-230.
[21]  Chen, C.-M., Wang, J.-Y. and Yu, C.-M. (2017) Assessing the Attention Levels of Students by Using a Novel Attention Aware System Based on Brainwave Signals. British Journal of Educational Technology, 48, 348-369.
https://doi.org/10.1111/bjet.12359
[22]  Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A.M. and D’Mello, S.K. (2017) Put Your Thinking Cap on: Detecting Cognitive Load Using EEG during Learning. Proceedings of the 7th International Learning Analytics & Knowledge Conference, Vancouver, 13-17 March 2017, 80-89.
https://doi.org/10.1145/3027385.3027431
[23]  Henrie, C.R., Halverson, L.R. and Graham, C.R. (2015) Measuring Student Engagement in Technology-Mediated Learning: A Review. Computers & Education, 90, 36-53.
https://doi.org/10.1016/j.compedu.2015.09.005
[24]  Nakamaru, S. (2011) Investment and Return. Journal of Research on Technology in Education, 44, 273-291.
https://doi.org/10.1080/15391523.2012.10782591
[25]  Yang, Y.-F. (2011) Engaging Students in an Online Situated Language Learning Environment. Computer Assisted Language Learning, 24, 181-198.
https://doi.org/10.1080/09588221.2010.538700
[26]  Azevedo, R. (2015) Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues. Educational Psychologist, 50, 84-94.
https://doi.org/10.1080/00461520.2015.1004069
[27]  Chen, P.-S.D., Lambert, A.D. and Guidry, K.R. (2010) Engaging Online Learners: The Impact of Web-Based Learning Technology on College Student Engagement. Computers & Education, 54, 1222-1232.
https://doi.org/10.1016/j.compedu.2009.11.008
[28]  Jaafar, S., Awaludin, N.S. and Bakar, N.S. (2014) Motivational and Self-Regulated Learning Components of Classroom Academic Performance. Journal of Educational Psychology, 82, 33-40.
[29]  Fredricks, J.A. and McColskey, W. (2012) The Measurement of Student Engagement: A Comparative Analysis of Various Methods and Student Self-Report Instruments. In: Christenson, S.L., Reschly, A.L. and Wylie, C., Eds., Handbook of Research on Student Engagement, Springer US, Boston, 763-782.
[30]  Wigfield, A., et al. (2008) Role of Reading Engagement in Mediating Effects of Reading Comprehension Instruction on Reading Outcomes. Psychology in the Schools, 45, 432-445.
https://doi.org/10.1002/pits.20307
[31]  Helme, S. and Clarke, D. (2001) Identifying Cognitive Engagement in the Mathematics Classroom. Mathematics Education Research Journal, 13, 133-153.
https://doi.org/10.1007/BF03217103
[32]  Alford, B.L., Rollins, K.B., Padrón, Y.N. and Waxman, H.C. (2016) Using Systematic Classroom Observation to Explore Student Engagement as a Function of Teachers’ Developmentally Appropriate Instructional Practices (DAIP) in Ethnically Diverse Pre-Kindergarten through Second-Grade Classrooms. Early Childhood Education Journal, 44, 623-635.
https://doi.org/10.1007/s10643-015-0748-8
[33]  Turner, J.C., Christensen, A., Kackar-Cam, H.Z., Trucano, M. and Fulmer, S.M. (2014) Enhancing Students’ Engagement: Report of a 3-Year Intervention with Middle School Teachers. American Educational Research Journal, 51, 1195-1226.
https://doi.org/10.3102/0002831214532515
[34]  Whitehill, J., Serpell, Z., Lin, Y., Foster, A. and Movellan, J.R. (2014) The Faces of Engagement: Automatic Recognition of Student Engagement from Facial Expressions. IEEE Transactions on Affective Computing, 5, 86-98.
https://doi.org/10.1109/TAFFC.2014.2316163
[35]  Benlamine, S., Bouslimi, S., Harley, J., Frasson, C. and Dufresne, A. (2015) Toward Brain-Based Gaming: Measuring Engagement during Gameplay. EdMedia: World Conference on Educational Media and Technology, Montréal, 22-25 June 2015, 717-722.
[36]  Berka, C., et al. (2007) EEG Correlates of Task Engagement and Mental Workload in Vigilance, Learning, and Memory Tasks. Aviation, Space, and Environmental Medicine, 78, B231-B244.
[37]  D’Mello, S., Chipman, P. and Graesser, A. (2007) Posture as a Predictor of Learner’s Affective Engagement. Proceedings of the 29th Annual Meeting of the Cognitive Science Society, Nashville, 1-4 August 2007, 905-910.
[38]  Pham, P. and Wang, J. (2015) Attentive Learner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking. International Conference on Artificial Intelligence in Education, Madrid, 21-25 June 2015, 367-376.
https://doi.org/10.1007/978-3-319-19773-9_37
[39]  Boucheix, J.-M., Lowe, R.K., Putri, D.K. and Groff, J. (2013) Cueing Animations: Dynamic Signaling Aids Information Extraction and Comprehension. Learning and Instruction, 25, 71-84.
https://doi.org/10.1016/j.learninstruc.2012.11.005
[40]  Lin, F.-R. and Kao, C.-M. (2018) Mental Effort Detection Using EEG Data in E-Learning Contexts. Computers & Education, 122, 63-79.
https://doi.org/10.1016/j.compedu.2018.03.020
[41]  Klem, G.H., Lüders, H.O., Jasper, H.H. and Elger, C. (1999) The Ten-Twenty Electrode System of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalography and Clinical Neurophysiology, 52, 3-6.
[42]  Chaouachi, M., Jraidi,I. and Frasson, C. (2015) MENTOR: A Physiologically Controlled Tutoring System. In: User Modeling, Adaptation and Personalization, Springer, Berlin, 56-67.
https://doi.org/10.1007/978-3-319-20267-9_5
[43]  Chaouachi, M., Jraidi, I. and Frasson, C. (2011) Modeling Mental Workload Using EEG Features for Intelligent Systems. In: User Modeling, Adaption and Personalization, Springer, Berlin, 50-61.
https://doi.org/10.1007/978-3-642-22362-4_5
[44]  Chaouachi, M., Chalfoun, P., Jraidi, I. and Frasson, C. (2010) Affect and Mental Engagement: Towards Adaptability for Intelligent Systems. 23rd International FLAIRS Conference, Florida, 19-21 May 2010, 6.

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