TY - GEN
T1 - An Image Recognition Practice for Using Mobile Phone During Class
AU - Lu, Chun Yi
AU - Lin, Yeong Ching
AU - Shaw, Heiu Jou
N1 - Funding Information:
Acknowledgment. This work supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant No. MOST 106-2511-S-346-002-MY2.
Funding Information:
This work supported by Ministry of Science and Technology, Taiwan, R.O.C. under Grant No. MOST 106-2511-S-346-002-MY2.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85076740018&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-35343-8_16
DO - 10.1007/978-3-030-35343-8_16
M3 - Conference contribution
AN - SCOPUS:85076740018
SN - 9783030353421
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 149
EP - 154
BT - Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings
A2 - Rønningsbakk, Lisbet
A2 - Wu, Ting-Ting
A2 - Sandnes, Frode Eika
A2 - Huang, Yueh-Min
PB - Springer
T2 - 2nd International Conference on Innovative Technologies and Learning, ICITL 2019
Y2 - 2 December 2019 through 5 December 2019
ER -