An Image Recognition Practice for Using Mobile Phone During Class

Chun Yi Lu; Yeong Ching Lin; Heiu Jou Shaw

doi:10.1007/978-3-030-35343-8_16

An Image Recognition Practice for Using Mobile Phone During Class

Chun Yi Lu, Yeong Ching Lin, Heiu Jou Shaw

Department of Systems and Naval Mechatronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings
Editors	Lisbet Rønningsbakk, Ting-Ting Wu, Frode Eika Sandnes, Yueh-Min Huang
Publisher	Springer
Pages	149-154
Number of pages	6
ISBN (Print)	9783030353421
DOIs	https://doi.org/10.1007/978-3-030-35343-8_16
Publication status	Published - 2019 Jan 1
Event	2nd International Conference on Innovative Technologies and Learning, ICITL 2019 - Tromsø, Norway Duration: 2019 Dec 2 → 2019 Dec 5

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11937 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Innovative Technologies and Learning, ICITL 2019
Country	Norway
City	Tromsø
Period	19-12-02 → 19-12-05

Fingerprint

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Cite this

Lu, C. Y., Lin, Y. C., & Shaw, H. J. (2019). An Image Recognition Practice for Using Mobile Phone During Class. In L. Rønningsbakk, T-T. Wu, F. E. Sandnes, & Y-M. Huang (Eds.), Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings (pp. 149-154). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11937 LNCS). Springer. https://doi.org/10.1007/978-3-030-35343-8_16

Lu, Chun Yi ; Lin, Yeong Ching ; Shaw, Heiu Jou. / An Image Recognition Practice for Using Mobile Phone During Class. Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings. editor / Lisbet Rønningsbakk ; Ting-Ting Wu ; Frode Eika Sandnes ; Yueh-Min Huang. Springer, 2019. pp. 149-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2110dcd8d3a64e348d7fcc722fcd0b7e,

title = "An Image Recognition Practice for Using Mobile Phone During Class",

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{\textquoteright} 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.",

author = "Lu, {Chun Yi} and Lin, {Yeong Ching} and Shaw, {Heiu Jou}",

year = "2019",

month = jan

day = "1",

doi = "10.1007/978-3-030-35343-8_16",

language = "English",

isbn = "9783030353421",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "149--154",

editor = "Lisbet R{\o}nningsbakk and Ting-Ting Wu and Sandnes, {Frode Eika} and Yueh-Min Huang",

booktitle = "Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings",

note = "2nd International Conference on Innovative Technologies and Learning, ICITL 2019 ; Conference date: 02-12-2019 Through 05-12-2019",

}

Lu, CY, Lin, YC & Shaw, HJ 2019, An Image Recognition Practice for Using Mobile Phone During Class. in L Rønningsbakk, T-T Wu, FE Sandnes & Y-M Huang (eds), Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11937 LNCS, Springer, pp. 149-154, 2nd International Conference on Innovative Technologies and Learning, ICITL 2019, Tromsø, Norway, 19-12-02. https://doi.org/10.1007/978-3-030-35343-8_16

An Image Recognition Practice for Using Mobile Phone During Class. / Lu, Chun Yi; Lin, Yeong Ching; Shaw, Heiu Jou.

Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings. ed. / Lisbet Rønningsbakk; Ting-Ting Wu; Frode Eika Sandnes; Yueh-Min Huang. Springer, 2019. p. 149-154 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11937 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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

PY - 2019/1/1

Y1 - 2019/1/1

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

UR - http://www.scopus.com/inward/citedby.url?scp=85076740018&partnerID=8YFLogxK

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 -

Lu CY, Lin YC, Shaw HJ. An Image Recognition Practice for Using Mobile Phone During Class. In Rønningsbakk L, Wu T-T, Sandnes FE, Huang Y-M, editors, Innovative Technologies and Learning - 2nd International Conference, ICITL 2019, Proceedings. Springer. 2019. p. 149-154. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-35343-8_16

Access to Document

10.1007/978-3-030-35343-8_16