What is it about?
One of the main problem that occurs in online education / learning is to understand the level of engagement the student. Analyzing students' emotional responses can provide valuable insights into their level of engagement. This not only offers instructors real-time feedback, but also enables them to refine their teaching methods and resources to better capture students' interest and enhance their learning experience.
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Why is it important?
We implemented three different deep learning models - ResNet50v2, VGG19, and Yolov8 - to understand students' emotions throughout the lecture and provide engagement level at the end. The model were trained on subset of data extracted from the extensive DAisEE and YaWDD datasets.
Perspectives
The rise of online learning following the pandemic has made it more challenging for instructors to gauge how well students are understanding the course, as there is less direct interaction. Therefore, assessing student engagement throughout the lecture is essential for instructors to better understand their learning progress and adjust their teaching strategies accordingly.
Mohit Marvania
George Mason University
Read the Original
This page is a summary of: YOLOv8-based emotion recognition for effective student engagement assessment in online learning environments, January 2025, American Institute of Physics,
DOI: 10.1063/5.0254176.
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