Eblob

It is a key topic for educators to obtain students' engagement in offline classrooms. However, in the context of the COVID-19 pandemic, teachers are confronting the challenge of understanding student engagement in an online education environment. Smart agents are able to automatically detect a student's engagement by combining emotion and attention, but the human agency is considered to be an essential role to achieve better interaction. We propose E-blob, a smart system that detects student engagement based on computer vision technology, with a blob on the interface explaining the algorithm in the user's interaction-attention continuum. The system is implemented locally with python and processing. For future work, the accuracy and user experience of the whole system needs to be evaluated.

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Eindhoven

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