Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors

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Journal of Behavioral and Brain Science

ISSN Print: 2160-5866
ISSN Online: 2160-5874
www.scirp.org/journal/jbbs
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Google-based Impact Factor: 0.86
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"Automatic Detection of Learner Engagement Using Machine Learning and Wearable Sensors"
written by Meredith Carroll, Mitchell Ruble, Mark Dranias, Summer Rebensky, Maria Chaparro, Joanna Chiang, Brent Winslow,
published by Journal of Behavioral and Brain Science, Vol.10 No.3, 2020

has been cited by the following article(s):

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[3]	Toward an Interactive Reading Experience: Deep Learning Insights and Visual Narratives of Engagement and Emotion
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[11]	Identification of heart rate change during the teaching process
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[12]	A survey on automatic engagement recognition methods: online and traditional classroom
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[14]	Research on student engagement in distance learning in sustainability science to design an online intelligent assessment system
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[15]	Real-time analysis of student's behavioural engagement in digital smart classrooms using fog computing and IoT devices
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[17]	A systematic review on machine learning models for online learning and examination systems
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[18]	Wearables for Engagement Detection in Learning Environments: A Review
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[22]	Multi-sensor eye-tracking systems and tools for capturing Student attention and understanding engagement in learning: A review
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[3]	Optimizing Human–Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis Sensors, 2024 DOI:10.3390/s24092817

[4]	Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms Sensors, 2024 DOI:10.3390/s24175487

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[7]	Future of service member monitoring: the intersection of biology, wearables and artificial intelligence BMJ Military Health, 2024 DOI:10.1136/military-2022-002306

[8]	MultiMediate 2023: Engagement Level Detection using Audio and Video Features Proceedings of the 31st ACM International Conference on Multimedia, 2023 DOI:10.1145/3581783.3612873

[9]	MultiMediate 2023: Engagement Level Detection using Audio and Video Features Proceedings of the 31st ACM International Conference on Multimedia, 2023 DOI:10.1145/3581783.3612873

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[13]	Research on student engagement in distance learning in sustainability science to design an online intelligent assessment system Frontiers in Psychology, 2023 DOI:10.3389/fpsyg.2023.1282386

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[16]	Wearables for Engagement Detection in Learning Environments: A Review Biosensors, 2022 DOI:10.3390/bios12070509

[17]	Intelligent Splicing Method of Virtual Reality Lingnan Cultural Heritage Panorama Based on Automatic Machine Learning Mobile Information Systems, 2021 DOI:10.1155/2021/8693436

[18]	Multi-Sensor Eye-Tracking Systems and Tools for Capturing Student Attention and Understanding Engagement in Learning: A Review IEEE Sensors Journal, 2021 DOI:10.1109/JSEN.2021.3105706

[19]	Automatic Estimation of Interpersonal Engagement During Naturalistic Conversation Using Dyadic Physiological Measurements Frontiers in Neuroscience, 2021 DOI:10.3389/fnins.2021.757381

[20]	Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning Computers, Materials & Continua, 2021 DOI:10.32604/cmc.2021.017966

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