02.02.2021

Tech Xplore: Deep learning-based assessment of student engagement could aid classroom research

Enkelejda Kasneci on the potential of machine-learning techniques

Past research has identified student engagement, or the extent to which students participate and are involved in classroom activities, as a crucial factor determining both the quality of education programs and the academic performance of individual students. As a result, many educators worldwide are actively trying to devise courses that maximize student engagement.

Assessing student engagement effectively and reliably, however, can be fairly challenging. 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.

Researchers at University of Tübingen and Leibniz Institute für Wissensmedien in Germany, as well as University of Colorado Boulder, have recently investigated the potential of machine-learning techniques for assessing student engagement in the context of classroom research. More specifically, they devised a deep-neural-network-based architecture that can estimate student engagement by analyzing video footage collected in classroom environments.

Read the full article on techexplore.com.