Author:
Patil Jayshree,Adamuthe Amol,Patil Sudarshan
Publisher
Springer Nature Singapore
Reference30 articles.
1. Sharma, P., Joshi, S., Gautam, S., Filipe, V., Reis, M.J.: Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning. arXiv preprint arXiv:1909.12913 (2019)
2. Zaletelj, J., Košir, A.: Predicting students’ attention in the classroom from Kinect facial and body features. EURASIP J. Image Video Process. 2017(1), 80 (2017)
3. Kamath, A., Biswas, A., Bala subramanian, V.: A crowdsourced approach to student engagement recognition in e-learning environments. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9. IEEE (2016)
4. Monkaresi, H., Bosch, N., Calvo, R.A., D’Mello, S.K.: Automated detection of engagement using video-based estimation of facial expressions and heart rate. IEEE Trans. Affect. Comput. 8(1), 15–28 (2016)
5. Kaur, A., Mustafa, A., Mehta, L., Dhall, A.: Prediction and localization of student engagement in the wild. In: 2018 Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2018)