Abstract
In the past, Student Engagements were measured in the form of statistical scales. In previous studies, some scholars divided the bad behaviors of students into 19 categories, covering 22 subcategories. These bad behaviors may represent a lack of either Student Engagements or intention to study the course. With the rise of artificial intelligence, some students’ lousy behavior recognition in the classroom can be used as the judgment standard of Student Engagements. In this work, we try to use image processing technology combined with machine learning and use SVM method to determine whether students have the use of mobile phones in the classroom. We divide the processing stage into several parts, namely pre-processing, segmentation, extract features, and machine learning. In the futures, we may use artificial intelligence to judge the dis-behavior of students during class; it is also possible to assist in the validation of research related to such scales in the past.
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
References
Berri, R.A., Silva, A.G., Parpinelli, R.S., Girardi, E.R.: A pattern recognition system for detecting use of mobile phones while driving. In: IEEE VISAPP Conference, Lisbon (2014)
Nagel, D.: The future of E-learning Is More Growth (2010). https://campustechnology.com/articles/2010/03/03/the-future-of-e-learning-is-more-growth.aspx
Downes, S.: E-Learning 2.0 eLearn Magazine. ACM, October 2005. https://elearnmag.acm.org/featured.cfm?aid=1104968
Fredricks, J.A., Wang, M.T., Linn, J.S., Hofkens, T.L., Sung, H., Parr, A.: Using qualitative methods to develop a survey measure of math and science engagement. Learn. Instr. 43, 5–15 (2016)
Kumar, V.: Computer-Supported Collaborative Learning: Issues for Research. Graduate Symposium, University of Saskatchewan 2996 (1996)
Sun, R.C.F., Shek, D.T.C.: Classroom misbehavior in the eyes of students: a qualitative study. Int. J. Child Health Hum. Dev. 6, 113–125 (2013)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-3264-1
Wang, M.T., Fredricks, J.A., Ye, F., Hofkens, T.L., Linn, J.S.: The math and science engagement scales: Scale development, validation, and psychometric properties. Learn. Instr. 43, 16–26 (2016)
Acknowledgment
This work supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant No. MOST 106-2511-S-346-002-MY2.
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
Lu, CY., Lin, YC., Shaw, HJ. (2019). An Image Recognition Practice for Using Mobile Phone During Class. In: Rønningsbakk, L., Wu, TT., Sandnes, F., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science(), vol 11937. Springer, Cham. https://doi.org/10.1007/978-3-030-35343-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-35343-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35342-1
Online ISBN: 978-3-030-35343-8
eBook Packages: Computer ScienceComputer Science (R0)