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Techniques to monitor the engagement and participation of students in the classroom over time, and without intruding or adversely impacting their learning experience, would thus be of great value, as they could be used to investigate the effectiveness of courses and education strategies.

More specifically, they devised a deep-neural-network-based architecture that can estimate student engagement by analyzing video footage collected in classroom environments.

“We used camera data collected during lessons to teach a deep-neural-network-based model to predict student engagement levels,” Enkelejda Kasneci the leading HCI researcher in the multidisciplinary team that carried out the study, told TechXplore.

“We trained our model on ground-truth data (e.g., expert ratings of students’ level of engagement based on the videos recorded in the classroom).

After this training, the model was able to predict, for instance, whether data obtained from a particular student at a particular point in time indicates high or low levels of engagement.”

The model devised by Kasneci and her colleagues can scan large datasets of videos shot in classroom environments and identify instances where student engagement was either high or low.

“For us as a research team, it is very important to stress that the goal is not to closely monitor specific students, but rather to develop intelligent engagement strategies for more effective instruction,” Gerjets explained.

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