Abstract
In this paper we present a tutoring system that automatically sequences the learning content according to the learners’ mental states. The system draws on techniques from Brain Computer Interface and educational psychology to automatically adapt to changes in the learners’ mental states such as attention and workload using electroencephalogram (EEG) signals. The objective of this system is to maintain the learner in a positive mental state throughout the tutoring session by selecting the next pedagogical activity that fits the best to his current state. An experimental evaluation of our approach involving two groups of learners showed that the group who interacted with the mental state-based adaptive version of the system obtained higher learning outcomes and had a better learning experience than the group who interacted with a non-adaptive version.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Pour, P.A., Hussain, M., AlZoubi, O., D’Mello, S., Calvo, R.A.: The impact of system feedback on learners’ affective and physiological states. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 264–273. Springer, Heidelberg (2010)
Banda, N., Robinson, P.: Multimodal affect recognition in intelligent tutoring systems. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part II. LNCS, vol. 6975, pp. 200–207. Springer, Heidelberg (2011)
Jraidi, I., Chaouachi, M., Frasson, C.: A hierarchical probabilistic framework for recognizing learners’ interaction experience trends and emotions. Advances in Human-Computer Interaction (2013)
D’Mello, S.K., Craig, S.D., Gholson, B., Franklin, S., Picard, R.W., Graesser, A.C.: Integrating affect sensors in an intelligent tutoring system. In: Proc of Affective Interactions: The Computer in the Affective Loop Workshop at International Conference on IUI, pp. 7-13 (2005)
Jraidi, I., Chaouachi, M., Frasson, C.: A dynamic multimodal approach for assessing learners’ interaction experience. In: Proc of ACM International Conference on Multimodal Interaction (2013)
Paas, F.G.: Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of educational psychology 84(4), 429 (1992)
Kalyuga, S., Chandler, P., Tuovinen, J., Sweller, J.: When problem solving is superior to studying worked examples. Journal of educational psychology 93(3), 579 (2001)
Berka, C., Levendowski, D.J., Ramsey, C.K., Davis, G., Lumicao, M.N., Stanney, K., Reeves, L., Regli, S.H., Tremoulet, P.D., Stibler, K.: Evaluation of an EEG workload model in an aegis simulation environment. In: Defense and Security Int. Soc. Optics and Photonics, pp.90-99 (2005)
Stevens, R., Galloway, T., Berka, C.: Integrating EEG models of cognitive load with machine learning models of scientific problem solving. In: Augmented Cognition: Past, Present and Future. Strategic Analysis, Inc., Arlington, pp. 55-65 (2006)
Sterman, M.B., Mann, C.A.: Concepts and applications of EEG analysis in aviation performance evaluation. Biological Psychology 40(1–2), 115–130 (1995)
Stevens, R.H., Galloway, T., Berka, C.: EEG-related changes in cognitive workload, engagement and distraction as students acquire problem solving skills. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 187–196. Springer, Heidelberg (2007)
Berka, C., Levendowski, D.J., Cvetinovic, M.M., Petrovic, M.M., Davis, G., Lumicao, M.N., Zivkovic, V.T., Popovic, M.V., Olmstead, R.: Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. International Journal of Human-Computer Interaction 17(2), 151–170 (2004)
Van Orden, K.F., Limbert, W., Makeig, S., Jung, T.-P.: Eye activity correlates of workload during a visuospatial memory task. Human Factors: The Journal of the Human Factors and Ergonomics Society 43(1), 111–121 (2001)
Wilson, G.F.: An analysis of mental workload in pilots during flight using multiple sychophysiological measures. Int. J. Aviat. Psychol. 12, 3–18 (2002)
Gevins, A., Smith, M.E.: Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical Issues in Ergonomics Science 4(1–2), 113–131 (2003)
Pope, A.T., Bogart, E.H., Bartolome, D.S.: Biocybernetic system evaluates indices of operator engagement in automated task. Biological psychology 40(1), 187–195 (1995)
Lubar, J.F.: Discourse on the development of EEG diagnostics and biofeedback for attention-deficit/hyperactivity disorders. Biofeedback and Self-regulation 16(3), 201–225 (1991)
Chaouachi, M., Jraidi, I., Frasson, C.: Modeling mental workload using EEG features for intelligent systems. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 50–61. Springer, Heidelberg (2011)
Hart, S.G., Staveland, L.E.: Development Of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Human Mental Workload 1(3), 139–183 (1988)
Rasmussen, C.E.: Gaussian processes for machine learning (2006)
Nguyen-Tuong, D., Peters, J.R., Seeger, M.: Local gaussian process regression for real time online model learning. In: Advances in Neural Information Processing Systems, pp.1193-1200 (2008)
Sweller, J.: Evolution of human cognitive architecture. Psychology of Learning and Motivation, 215-266 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Chaouachi, M., Jraidi, I., Frasson, C. (2015). MENTOR: A Physiologically Controlled Tutoring System. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-20267-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20266-2
Online ISBN: 978-3-319-20267-9
eBook Packages: Computer ScienceComputer Science (R0)