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Use Machine Learning to Predict Primary School Students’ Level of Learning Engagement

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Yujie Yan, and

Hongjie OuyangAuthors Info & Claims

ICETM '21: Proceedings of the 2021 4th International Conference on Education Technology Management

December 2021

Pages 20 - 24

https://doi.org/10.1145/3510309.3510313

Published: 14 March 2022 Publication History

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Cited By

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Mupaikwa E(2024)Artificial Intelligence-Driven Instruction and Its Impact on Heutagogy and Student EngagementAI Algorithms and ChatGPT for Student Engagement in Online Learning10.4018/979-8-3693-4268-8.ch007(101-123)Online publication date: 14-Jun-2024
https://doi.org/10.4018/979-8-3693-4268-8.ch007
Öztüre Yavuz GAkçapınar GÇıralı Sarıca HKoçak Usluel Y(2024)Investigating features that play a role in predicting gifted student engagement using machine learning: Video log and self-report dataEducation and Information Technologies10.1007/s10639-024-12490-9Online publication date: 8-Feb-2024
https://doi.org/10.1007/s10639-024-12490-9
Podder DSinha MBasu ADasgupta T(2023)Multisensory computer-based system for teaching sentence reading in Hindi and Bangla to children with dyslexiaTechnology and Disability10.3233/TAD-23000535:4(255-278)Online publication date: 27-Dec-2023
https://doi.org/10.3233/TAD-230005

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Use Machine Learning to Predict Primary School Students’ Level of Learning Engagement

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ICETM '21: Proceedings of the 2021 4th International Conference on Education Technology Management

December 2021

323 pages

ISBN:9781450385800

DOI:10.1145/3510309

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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Published: 14 March 2022

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ICETM'21: 2021 4th International Conference on Education Technology Management

December 17 - 19, 2021

Tokyo, Japan

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Mupaikwa E(2024)Artificial Intelligence-Driven Instruction and Its Impact on Heutagogy and Student EngagementAI Algorithms and ChatGPT for Student Engagement in Online Learning10.4018/979-8-3693-4268-8.ch007(101-123)Online publication date: 14-Jun-2024
https://doi.org/10.4018/979-8-3693-4268-8.ch007
Öztüre Yavuz GAkçapınar GÇıralı Sarıca HKoçak Usluel Y(2024)Investigating features that play a role in predicting gifted student engagement using machine learning: Video log and self-report dataEducation and Information Technologies10.1007/s10639-024-12490-9Online publication date: 8-Feb-2024
https://doi.org/10.1007/s10639-024-12490-9
Podder DSinha MBasu ADasgupta T(2023)Multisensory computer-based system for teaching sentence reading in Hindi and Bangla to children with dyslexiaTechnology and Disability10.3233/TAD-23000535:4(255-278)Online publication date: 27-Dec-2023
https://doi.org/10.3233/TAD-230005

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Integrating learning and engagement in narrative-centered learning environments

Integrating learning, problem solving, and engagement in narrative-centered learning environments

Machine Learning: The State of the Art

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