research-article

Teaching Video Analytics Based on Student Spatial and Temporal Behavior Mining

Authors:

Heqing YaAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 635 - 642

https://doi.org/10.1145/2671188.2749357

Published: 22 June 2015 Publication History

Abstract

In this paper, we propose to mine the class videos and analyze the behaviors of students to obtain the information on the focus of the students during one class. As a methodological contribution, we investigate face detection, tracking, and verification techniques to measure student engagement from the video records. We also analyze several behaviors of a single student or groups of students, such as temporal behaviors and spatial behaviors. Furthermore, we mine the relationship between students' behaviors and grades throughout the whole semester, including 16 weeks. This work can help teachers to improve the quality of class, find students who tend to get bad grades and give these students timely help. Experimental results on the test videos of different kinds of classes demonstrate the effectiveness of the proposed method.

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Cited By

Trung BSon NTung NSon KAnh BLam P(2023)Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 20222023 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA58478.2023.10212273(1-15)Online publication date: 15-Jul-2023
https://doi.org/10.1109/ISIEA58478.2023.10212273
Thomas CJayagopi D(2022)Predicting Presentation Skill of a Speaker Using Automatic Speaker and Audience MeasurementIEEE Transactions on Learning Technologies10.1109/TLT.2022.317160115:3(350-363)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TLT.2022.3171601
Cukurova MZhou QSpikol DLandolfi LRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Modelling collaborative problem-solving competence with transparent learning analyticsProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375484(270-275)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375484
Show More Cited By

Index Terms

Teaching Video Analytics Based on Student Spatial and Temporal Behavior Mining

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Published In

cover image ACM Conferences

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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ICMR '15

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SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Trung BSon NTung NSon KAnh BLam P(2023)Educational Data Mining: A Systematic Review on the Applications of Classical Methods and Deep Learning Until 20222023 IEEE Symposium on Industrial Electronics & Applications (ISIEA)10.1109/ISIEA58478.2023.10212273(1-15)Online publication date: 15-Jul-2023
https://doi.org/10.1109/ISIEA58478.2023.10212273
Thomas CJayagopi D(2022)Predicting Presentation Skill of a Speaker Using Automatic Speaker and Audience MeasurementIEEE Transactions on Learning Technologies10.1109/TLT.2022.317160115:3(350-363)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TLT.2022.3171601
Cukurova MZhou QSpikol DLandolfi LRensing CDrachsler HKovanović VPinkwart NScheffel MVerbert K(2020)Modelling collaborative problem-solving competence with transparent learning analyticsProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375484(270-275)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3375462.3375484
Xu XTeng X(2020)Classroom Attention Analysis Based on Multiple Euler Angles Constraint and Head Pose EstimationMultiMedia Modeling10.1007/978-3-030-37731-1_27(329-340)Online publication date: 5-Jan-2020
https://dl.acm.org/doi/10.1007/978-3-030-37731-1_27
Chua YDauwels JTan SHsiao SCunningham JMcCarthy KLynch GBrooks CFerguson RHoppe U(2019)Technologies for automated analysis of co-located, real-life, physical learning spacesProceedings of the 9th International Conference on Learning Analytics & Knowledge10.1145/3303772.3303811(11-20)Online publication date: 4-Mar-2019
https://dl.acm.org/doi/10.1145/3303772.3303811
Gupta SAshwin TGuddeti R(2019)Students’ affective content analysis in smart classroom environment using deep learning techniquesMultimedia Tools and Applications10.1007/s11042-019-7651-z78:18(25321-25348)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11042-019-7651-z
Thomas CNair NJayagopi DD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)Predicting Engagement Intensity in the Wild Using Temporal Convolutional NetworkProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3264984(604-610)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3264984
Thomas CD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)Multimodal Teaching and Learning Analytics for Classroom and Online Educational SettingsProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3264969(542-545)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3264969
Thomas CJayagopi DVolpe GGori MBianchi-Berthouze NBaud-Bovy GAlborno PVolta E(2017)Predicting student engagement in classrooms using facial behavioral cuesProceedings of the 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education10.1145/3139513.3139514(33-40)Online publication date: 13-Nov-2017
https://dl.acm.org/doi/10.1145/3139513.3139514

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