An Novel End-to-end Network for Automatic Student Engagement Recognition

Automatically recognizing and improving students' engagement is conducive to improving students' academic performance and less dropout rate. After analyzing the advantages and disadvantages of some methods in the field of automatic recognition of students' engagement, in this paper, a...

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Bibliographic Details
Main Authors: Zhang, Hao, Xiao, Xiaofan, Huang, Tao, Liu, Sanya, Xia, Yu, Li, Jia
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Brain modeling
Convolution
dropoutrate
engagement recognition
Feature extraction
I3D
Solid modeling
Three-dimensional displays
Two dimensional displays
weighted I3D
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Summary:Automatically recognizing and improving students' engagement is conducive to improving students' academic performance and less dropout rate. After analyzing the advantages and disadvantages of some methods in the field of automatic recognition of students' engagement, in this paper, a novel end-to-end student engagement recognition network is proposed. We innovatively improve and optimize the excellent model Inflated 3D Convolutional Network (I3D) used in the field of action classification and apply it to the field of automatic students' engagement recognition. In the end, we have achieved ideal results in the student engagement recognition dataset DAiSEE, with an accuracy of 98.82 % in binary engagement classification, 4.42% higher than the benchmark.
ISSN:2377-844X
DOI:10.1109/ICEIEC.2019.8784507