Abstract
Affect detection is a challenging problem, even more in educational contexts, where emotions are spontaneous and usually subtle. In this paper, we propose a two-stage detection approach based on an initial binary discretization followed by a specific emotion prediction stage. The binary classification method uses several distinct sources of information to detect and filter relevant time slots from an affective point of view. An accuracy close to 75% at detecting whether the learner has felt an educationally relevant emotion on 20 second time slots has been obtained. These slots can then be further analyzed by a second classifier, to determine the specific user emotion.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective learning—a manifesto. BT Technol. J. 22, 253–269 (2004)
Saneiro, M., Santos, O.C., Salmeron-Majadas, S., Boticario, J.G.: Towards Emotion Detection in Educational Scenarios from Facial Expressions and Body Movements through Multimodal Approaches. Sci. World J. 2014, e484873 (2014)
Santos, O.C., Salmeron-Majadas, S., Boticario, J.G.: Emotions Detection from Math Exercises by Combining Several Data Sources. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 742–745. Springer, Heidelberg (2013)
Salmeron-Majadas, S., Santos, O.C., Boticario, J.G.: An Evaluation of Mouse and Keyboard Interaction Indicators towards Non-intrusive and Low Cost Affective Modeling in an Educational Context. Procedia Comput. Sci. 35, 691–700 (2014)
Arevalillo-Herráez, M., Arnau, D., Marco-Giménez, L.: Domain-specific knowledge representation and inference engine for an intelligent tutoring system. Knowl.-Based Syst. 49, 97–105 (2013)
Ayesh, A., Arevalillo-Herraez, M., Ferri, F.J.: Cognitive reasoning and inferences through psychologically based personalised modelling of emotions using associative classifiers. 2014 IEEE 13th International Conference on Cognitive Informatics Cognitive Computing (ICCI*CC). pp. 67–72 (2014).
Andreassi, J.L.: Psychophysiology: Human behavior and physiological response, 4th edn. Lawrence Erlbaum Associates Publishers, Mahwah (2000)
D’Mello, S.K.: Emotional rollercoasters: day differences in affect incidence during learning. In: The Twenty-Seventh International Flairs Conference (2014)
Blanchard, N., Bixler, R., Joyce, T., D’Mello, S.: Automated Physiological-Based Detection of Mind Wandering during Learning. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 55–60. Springer, Heidelberg (2014)
Jraidi, I., ne, Chaouachi, M., Frasson, C.: A Hierarchical Probabilistic Framework for Recognizing Learners’ Interaction Experience Trends and Emotions. Adv. Hum.-Comput. Interact. 2014, e632630 (2014)
Novak, D., Mihelj, M., Munih, M.: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interact. Comput. 24, 154–172 (2012)
Aldao, A.: The Future of Emotion Regulation Research Capturing Context. Perspect. Psychol. Sci. 8, 155–172 (2013)
Ekman, P., Friesen, W.V.: Manual for the Facial Action Coding System. Consulting Psychologists Press, Palo Alto, Calif, USA
Szwoch, M.: FEEDB: A multimodal database of facial expressions and emotions. In: 2013 The 6th International Conference on Human System Interaction (HSI), pp. 524–531 (2013)
Ioannou, S.V., Raouzaiou, A.T., Tzouvaras, V.A., Mailis, T.P., Karpouzis, K.C., Kollias, S.D.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18, 423–435 (2005)
Afzal, S., Robinson, P.: Designing for Automatic Affect Inference in Learning Environments. Educ. Technol. Soc. 14, 21–34 (2011)
D’mello, S., Graesser, A.: AutoTutor and Affective Autotutor: Learning by Talking with Cognitively and Emotionally Intelligent Computers That Talk Back. ACM Trans Interact Intell Syst. 2, 23:1–23:39 (2013)
Bahreini, K., Nadolski, R., Westera, W.: Towards multimodal emotion recognition in e-learning environments. Interact. Learn. Environ. 0, 1–16 (2014)
Felipe, D.A.M., Gutierrez, K.I.N., Quiros, E.C.M., Vea, L.A.: Towards the Development of Intelligent Agent for Novice C/C++ Programmers through Affective Analysis of Event Logs. Proc. Int. MultiConference Eng. Comput. Sci. 1 (2012)
Arnau, D., Arevalillo-Herráez, M., Puig, L., González-Calero, J.A.: Fundamentals of the design and the operation of an intelligent tutoring system for the learning of the arithmetical and algebraic way of solving word problems. Comput. Educ. 63, 119–130 (2013)
Más, M.A.M., Alonso, Á.V.: Validación de una Escala de Motivación de Logro. Psicothema 10, 333–351 (1998)
Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry. 25, 49–59 (1994)
Porayska-Pomsta, K., Mavrikis, M., Mello, S., Conati, C., Pomsta, K.: Knowledge elicitation methods for affect modelling in education. Int. J. Artif. Intell. Educ. 22, 107–140 (2013)
DMello, S.K., Dowell, N., Graesser, A.: Unimodal and Multimodal Human Perceptionof Naturalistic Non-Basic Affective Statesduring Human-Computer Interactions. IEEE Trans. Affect. Comput. 4, 452–465 (2013)
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980)
Schröder, M., Baggia, P., Burkhardt, F., Pelachaud, C., Peter, C., Zovato, E.: EmotionML – An Upcoming Standard for Representing Emotions and Related States. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 316–325. Springer, Heidelberg (2011)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, Second Edition. Morgan Kaufmann (2005)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. J Mach Learn Res. 3, 1157–1182 (2003)
Mukhopadhyay, S.C.: Wearable Sensors for Human Activity Monitoring: A Review. IEEE Sens. J. 15, 1321–1330 (2015)
Koo, H.R., Lee, Y.-J., Gi, S., Khang, S., Lee, J.H., Lee, J.-H., Lim, M.-G., Park, H.-J., Lee, J.-W.: The Effect of Textile-Based Inductive Coil Sensor Positions for Heart Rate Monitoring. J. Med. Syst. 38, 1–12 (2014)
Suau, X., Ruiz-Hidalgo, J., Casas, J.R.: Real-Time Head and Hand Tracking Based on 2.5D Data. IEEE Trans. Multimed. 14, 575–585 (2012)
Santos, O.C., Boticario, J.G.: Practical guidelines for designing and evaluating educationally oriented recommendations. Comput. Educ. 81, 354–374 (2015)
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
Salmeron-Majadas, S. et al. (2015). Filtering of Spontaneous and Low Intensity Emotions in Educational Contexts. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-19773-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19772-2
Online ISBN: 978-3-319-19773-9
eBook Packages: Computer ScienceComputer Science (R0)