会议论文

Zhang, Hao

英文

PROCEEDINGS OF 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2019)

2377-8431

2019

338-341

IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC)

IEEE International Conference on Electronics Information and Emergency Communication

JUL 12-14, 2019

Beijing, PEOPLES R CHINA

[Huang, Tao;Mei, Yunshan;Zhang, Hao;Liu, Sanya;Yang, Huali] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.^[Huang, Tao;Mei, Yunshan;Zhang, Hao;Liu, Sanya;Yang, Huali] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.

Inst Elect & Elect Engineers, IEEE Beijing Sect

Wenzheng, L Guomin, Z

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

978-1-7281-1190-2

National Key Research and Development Program of China [2017YFB1401300, 2017YFB1401304]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61702211, L1724007]; Hubei Provincial Science and Technology Program of China [2017AKA191]; Self-Determined Research Funds of CCNU from the Colleges' Basic Research [CCNU17QN0004, CCNU17GF0002, CCNU19QN040]

本校为第一且通讯机构

Engagement is an important measure of users' learning experience in online learning environment. Improving the accuracy of engagement recognition can help the instructors get timely feedback on the courses, optimize the recommendation strategies of online learning platforms and enhance users' learning experience. In this paper, we propose a novel model: Deep Engagement Recognition Network (DERN) which combines temporal convolution, bidirectional LSTM and attention mechanism to identify the degree of engagement based on the features captured by OpenFace. In order to verify the validity and stab...