Abstract
Analysis of student concentration can help to enhance the learning process. Emotions are directly related and directly reflect students’ concentration. This task is particularly difficult to implement in an e-learning environment, where the student stands alone in front of a computer. In this paper, a prototype system is proposed to figure out the concentration level in real-time from the expressed facial emotions during a lesson. An experiment was performed to evaluate the prototype system that was implemented using a client-side application that uses the C# code available in Microsoft Azure Emotion API. We have found that the emotions expressed are correlated with the concentration of the students, and devised three distinct levels of concentration (high, medium, and low).
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
Similar content being viewed by others
References
Blamire, R., Kearney, C., Quittre, V., Gaer, E., Monseur, C.: The use of ICT in education: a survey of schools. Eur. J. Educ. 11–27 (2013). https://doi.org/10.1111/ejed.12020
Pedró, F.: The new millennium learners: challenging our views on ICT and learning (2006)
Prensky, M.: Digital natives, digital immigrants, part ii: do they really think. Horizon 9(6), 1–6 (2001)
Papert, S.: The Children’s Machine: Rethinking School in the Age of the Computer. Basic Books, New York (1993)
Hayes, D.: ICT and learning: lessons from Australian classrooms. Comput. Educ. 2(49), 385–395 (2007)
Arkorful, V., Abaidoo, N.: The role of e-learning, the advantages and disadvantages of its adoption in higher education. Int. J. Educ. Res. 2(12), 397–410 (2014)
Dewey, B., DeBlois, P.: Top-ten IT issues. EDUCAUSE Rev. 41(3), 58–79 (2006)
Holmes, B., Gardner, J.: E-Learning: Concepts and Practice. Sage Publications Ltd., London (2006)
Smedley, J.: Modeling the impact of knowledge management using technology. OR Insight 23, 233–250 (2010)
Wagner, N., Hassnein, K., Head, M.: Who is responsible for e-learning success in higher education? A stakeholders’ analysis. Educ. Technol. Soc. 11(3), 26–36 (2008)
Perrin, D., Perrin, E., Muirhead, B., Betz, M.: Int. J. Instr. Technol. Distance Learn. 12(1) (2015). Publisher’s Declaration
Yewale, P., Zure, S., Awat, A., Kale, R.: Emotion recognition using image processing. Imperial J. Interdisciplinary Res. 3(5) (2017)
Ekman, P.: Universal facial expressions of emotions. California Mental Health Res. Digest 8(4), 151–158 (1970)
Pekrun, R.: The impact of emotions on learning and achievement: towards a theory of cognitive/motivational mediators. Appl. Psychol. 41 (4), 359–376 (1992)
Ekman, P., Friesen, W.: Facial Action Coding System: Investigator’s Guide. Consulting Psychologists Press, Palo Alto (1978)
Du, S., Tao, Y., Martinez, A.: Compound facial expressions of emotion. Proc. Natl. Acad. Sci. U.S.A. 111(15), 1454–1462 (2014)
Benitez, Q.C., Srinivasan, R., Martinez, A.: EmotioNet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, pp. 5562–5570 (2016)
De, A., Saha, A., Pal, M.: A human facial expression recognition model based on eigen face approach. Procedia Comput. Sci. 45, 282–289 (2015)
Maya, V., Pai, R., Pai, M.: Automatic facial expression recognition using DCNN. Procedia Comput. Sci. 93, 453–461 (2016)
Li, J., et al.: Facial expression recognition with faster R-CNN. Procedia Comput. Sci. 107, 135–140 (2017)
Ko, B.: A brief review of facial emotion recognition based on visual information. Sensors 18(2), E401 (2018)
Cha, S., Kim, W.: Analyze the learner’s concentration using detection of facial feature points. Adv. Sci. Technol. Lett. 92, 72–76 (2015)
Cha, S., Kim, W.: The analysis of learner’s concentration by facial expression changes & movements. Int. J. Appl. Eng. Res. 11(23), 11344–11349 (2016)
Yi, J., Sheng, B., Shen, R., Lin, W., Wu, E.: Real time learning evaluation based on gaze tracking. In: 14th International Conference on Computer-Aided Design and Computer Graphics, Shanghai, pp. 157–164 (2015)
Bosch, N., et al.: Detecting student emotions in computer-enabled classrooms. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 4125–4129 (2016)
Hsu, C., Chen, H., Su, Y., Huang, K., Huang, Y.: Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom. Sensors 12(10), 14158–14178 (2012)
Liu, N., Chiang, C., Chu, H.: Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors 13(8), 10273–10286 (2013)
Acknowledgments
This work was supported by Project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016” financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, P., Esengönül, M., Khanal, S.R., Khanal, T.T., Filipe, V., Reis, M.J.C.S. (2019). Student Concentration Evaluation Index in an E-learning Context Using Facial Emotion Analysis. In: Tsitouridou, M., A. Diniz, J., Mikropoulos, T. (eds) Technology and Innovation in Learning, Teaching and Education. TECH-EDU 2018. Communications in Computer and Information Science, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-20954-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-20954-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20953-7
Online ISBN: 978-3-030-20954-4
eBook Packages: Computer ScienceComputer Science (R0)