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Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms

Authors:

Sidney K. D'mello,

Jaclyn Ocumpaugh,

Valerie ShuteAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 6, Issue 2

Article No.: 17, Pages 1 - 26

https://doi.org/10.1145/2946837

Published: 20 July 2016 Publication History

Abstract

Affect detection is a key component in intelligent educational interfaces that respond to students’ affective states. We use computer vision and machine-learning techniques to detect students’ affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. We collected data in the real-world environment of a school computer lab with up to 30 students simultaneously playing the game while moving around, gesturing, and talking to each other. The results were cross-validated at the student level to ensure generalization to new students. Classification accuracies, quantified as area under the receiver operating characteristic curve (AUC), were above chance (AUC of 0.5) for all the affective states observed, namely, boredom (AUC = .610), confusion (AUC = .649), delight (AUC = .867), engagement (AUC = .679), frustration (AUC = .631), and for off-task behavior (AUC = .816). Furthermore, the detectors showed temporal generalizability in that there was less than a 2% decrease in accuracy when tested on data collected from different times of the day and from different days. There was also some evidence of generalizability across ethnicity (as perceived by human coders) and gender, although with a higher degree of variability attributable to differences in affect base rates across subpopulations. In summary, our results demonstrate the feasibility of generalizable video-based detectors of naturalistic affect in a real-world setting, suggesting that the time is ripe for affect-sensitive interventions in educational games and other intelligent interfaces.

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Cited By

Mahmood NBhatti SDawood HPradhan MAhmad H(2024)Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning ModelsAlgorithms10.3390/a1710045817:10(458)Online publication date: 16-Oct-2024
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Patidar PNgoon TZimmerman JOgan AAgarwal Y(2024)ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing SystemsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595868:2(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659586
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Index Terms

Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 6, Issue 2

Regular Articles, Special Issue on Highlights of IUI 2015 (Part 2 of 2) and Special Issue on Highlights of ICMI 2014 (Part 1 of 2)

August 2016

282 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/2974721

Editor:
Michelle Zhou
Juji, Inc., USA

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Copyright © 2016 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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Publication History

Published: 20 July 2016

Accepted: 01 March 2016

Revised: 01 January 2016

Received: 01 July 2015

Published in TIIS Volume 6, Issue 2

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Cited By

Mahmood NBhatti SDawood HPradhan MAhmad H(2024)Measuring Student Engagement through Behavioral and Emotional Features Using Deep-Learning ModelsAlgorithms10.3390/a1710045817:10(458)Online publication date: 16-Oct-2024
https://doi.org/10.3390/a17100458
Patidar PNgoon TZimmerman JOgan AAgarwal Y(2024)ClassID: Enabling Student Behavior Attribution from Ambient Classroom Sensing SystemsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595868:2(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659586
Singh KSamui AMukul MMisra CGoswami B(2024)Usability Evaluation of E-Learning Platforms Using UX/UI Design and ML Technique2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC60049.2024.10507912(1-6)Online publication date: 27-Jan-2024
https://doi.org/10.1109/ASSIC60049.2024.10507912
Abdelkawy AFarag AAlkabbany IAli AForeman CTretter THindy N(2024)Measuring student behavioral engagement using histogram of actionsPattern Recognition Letters10.1016/j.patrec.2024.11.002186(337-344)Online publication date: Oct-2024
https://doi.org/10.1016/j.patrec.2024.11.002
Frenzel ADindar MPekrun RReck CMarx A(2024)Joy is reciprocally transmitted between teachers and students: Evidence on facial mimicry in the classroomLearning and Instruction10.1016/j.learninstruc.2024.10189691(101896)Online publication date: Jun-2024
https://doi.org/10.1016/j.learninstruc.2024.101896
Alkabbany IAli AForeman CTretter THindy NFarag A(2023)An Experimental Platform for Real-Time Students Engagement Measurements from Video in STEM ClassroomsSensors10.3390/s2303161423:3(1614)Online publication date: 2-Feb-2023
https://doi.org/10.3390/s23031614
Hur PMachaka NKrist CBosch NHilliger IKhosravi HRienties BDawson S(2023)Informing Expert Feature Engineering through Automated Approaches: Implications for Coding Qualitative Classroom Video DataLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576090(630-636)Online publication date: 13-Mar-2023
https://dl.acm.org/doi/10.1145/3576050.3576090
Kar PSingh SMandal AChattopadhyay SChakraborty S(2023)ExpresSense: Exploring a Standalone Smartphone to Sense Engagement of Users from Facial Expressions Using Acoustic SensingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581235(1-18)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581235
Sümer ÖGoldberg PD’Mello SGerjets PTrautwein UKasneci E(2023)Multimodal Engagement Analysis From Facial Videos in the ClassroomIEEE Transactions on Affective Computing10.1109/TAFFC.2021.312769214:2(1012-1027)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TAFFC.2021.3127692
Zambrano ABaker RLan A(2023)Active Learning for a Classroom Observer who Can’t Time Travel2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)10.1109/ACIIW59127.2023.10388151(1-8)Online publication date: 10-Sep-2023
https://doi.org/10.1109/ACIIW59127.2023.10388151
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