Research Center for Computing and Multimedia Studies, Hosei University, Japan
In the current complicated COVID epidemic, many educational institutions have applied online teaching. Monitoring and analyzing students' academic performance in the online environment is essential. Thus, we developed this web-based application to help administrators and teachers capture students' learning performance. Then lecturers can improve more appropriate teaching methods.
We apply machine learning techniques in this research to exploit student features such as facial features, emotional features, eye gaze, eye movement, and so on. In addition, we design a suitable mapping method to assess student engagement throughout the class.
Techniques :
Emotion Detection,
Face Recognition,
Face Re-identification,
Eyegaze Tracking,
Programming Languages and frameworks :
Python,
Flask,
Sqlite3,
OpenCV,
Tensorflow,
HTML/CSS,
Javascript/AJAX,
Plotly
DEMO VIDEO
DEMO GALLERY
Supervisor
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HASNINE MOHAMMAD NEHAL (ハスナイン モハーマド ネハル)
Associate Professor at the Research Center for Computing and Multimedia Studies of Hosei University, Japan. |