TY - GEN
T1 - Concentration Monitoring in Online Classes for Smart Education Applications Based on Neural Network and C-PAD Emotion Model
AU - Li, Jiaxuan
AU - Lai, Haijian
AU - Pang, Patrick Cheong Iao
AU - Law, K. L.Eddie
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/12/23
Y1 - 2022/12/23
N2 - The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time.
AB - The arrival of COVID-19 has changed the way traditional classes are conducted, and online teaching has never been more popular. While there are many advantages to online teaching, there are also extremely obvious disadvantages, one of which is the tendency to lack concentration. For this reason, this study uses video images from the DAiSee dataset, a new sampling script, deep learning neural networks, and a new PAD emotion model to systematically assess student concentration. Our test set uses 21 short videos from the DAISee dataset, sampling a total of 1,866 frames. The final results showed that the accuracy of the neural network was approximately 80%. The results of the test set on the PAD model showed that the percentage of attentive listeners was 65.9%, while the percentage of highly inattentive listeners was 6.2%. This study constructed a complete concentration monitoring system for online classrooms centred on smart education which can provide the information of students' concentration in real time.
KW - Emotion recognition
KW - PAD model
KW - neural network
KW - online classroom
KW - smart education
UR - http://www.scopus.com/inward/record.url?scp=85152128660&partnerID=8YFLogxK
U2 - 10.1145/3582197.3582230
DO - 10.1145/3582197.3582230
M3 - Conference contribution
AN - SCOPUS:85152128660
T3 - ACM International Conference Proceeding Series
SP - 190
EP - 196
BT - Proceedings of the 2022 10th International Conference on Information Technology
PB - Association for Computing Machinery
T2 - 10th International Conference on Information Technology: IoT and Smart City, ICIT 2022
Y2 - 23 December 2022 through 26 December 2022
ER -