TY - GEN
T1 - Preliminary Design of an AI Service to Assist Self-regulated Learning by Edge Computing
AU - Chen, Eason
AU - Tseng, Yuen Hsien
AU - You, Yu Tang
AU - Lo, Kuo Ping
AU - Lin, Chris
N1 - Funding Information:
Acknowledgement. This work was partially supported by the Ministry of Science and Technology of Taiwan (R.O.C.) under Grants 109-2410-H-003-123-MY3.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - When sitting in front of a computer screen for online learning, students can be easily distracted by other sources on the Internet. To help students improve their online learning experience, we implemented a highly accessible AI service, which collected facial data from the web camera to assist self-regulated learning for students with privacy protection by way of edge computing. The service can capture self-learning metrics such as eye gazing points and facial expressions. Then the captured facial streaming data can be played back by the user. All steps are done locally at the users’ browser. The learners can review their online learning process to improve their learning efficiency through an interactive interface. Our preliminary evaluation showed promising feedback from real users.
AB - When sitting in front of a computer screen for online learning, students can be easily distracted by other sources on the Internet. To help students improve their online learning experience, we implemented a highly accessible AI service, which collected facial data from the web camera to assist self-regulated learning for students with privacy protection by way of edge computing. The service can capture self-learning metrics such as eye gazing points and facial expressions. Then the captured facial streaming data can be played back by the user. All steps are done locally at the users’ browser. The learners can review their online learning process to improve their learning efficiency through an interactive interface. Our preliminary evaluation showed promising feedback from real users.
KW - Artificial Intelligence (AI)
KW - E-learning
KW - Edge computing
KW - Eye Tracking
KW - Online learning
KW - Self-regulated learning
UR - http://www.scopus.com/inward/record.url?scp=85135958781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135958781&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-11647-6_119
DO - 10.1007/978-3-031-11647-6_119
M3 - Conference contribution
AN - SCOPUS:85135958781
SN - 9783031116469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 577
EP - 581
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
Y2 - 27 July 2022 through 31 July 2022
ER -