@inproceedings{9834cda5b8c44cc3b2857054cb54a1b8,
title = "A deep learning model for automatic evaluation of academic engagement",
abstract = "This paper proposed a deep learning model for automatic evaluation of academic engagement based on video data analysis. A coding system based on the BROMP standard for behavioral, emotional, and cognitive states was defined to code typical videos in an autonomous learning environment. Then after the key points of human skeletons were extracted from these videos using pose estimation technology, deep learning methods were used to realize the effective recognition and judgment of motion and emotions. Based on this, an analysis and evaluation of learners' learning states was accomplished, and a prototype of academic engagement evaluation system was successfully established eventually.",
keywords = "Academic Engagement, BROMP, Deep Learning, Feature Engineering",
author = "Chen Sun and Fan Xia and Ye Wang and Yan Liu and Weining Qian and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery. All rights reserved.; 5th Annual ACM Conference on Learning at Scale, L at S 2018 ; Conference date: 26-06-2018 Through 28-06-2018",
year = "2018",
month = jun,
day = "26",
doi = "10.1145/3231644.3231689",
language = "英语",
series = "Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018",
publisher = "Association for Computing Machinery, Inc",
booktitle = "Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018",
}