[Project] MOEMO: A Web-Based Application To Measure Student Engagement In Online Learning Environment

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 GALLERY

1 / 7
MOEMO Dashboard
2 / 7
Overview Visualization
3 / 7
Student Visualization 1
4 / 7
Student Visualization 2
5 / 7
Video Demo 1
6 / 7
Video Demo 2
7 / 7
Video Demo 3
Supervisor
Nehal Hasnine 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.