Teacher's Perception in the Classroom
This work addresses the need for efficient tools in educational research to understand teacher effectiveness, though it is incremental as it applies existing computer vision methods to a new domain.
The paper tackled the problem of analyzing teachers' attentional processes in classrooms using mobile eye-tracking data, which is time-consuming to analyze manually, by introducing a computer vision approach that detects and clusters student faces to calculate attentional focus per student, achieving automated analysis in a standardized small group setting.
The ability for a teacher to engage all students in active learning processes in classroom constitutes a crucial prerequisite for enhancing students achievement. Teachers' attentional processes provide important insights into teachers' ability to focus their attention on relevant information in the complexity of classroom interaction and distribute their attention across students in order to recognize the relevant needs for learning. In this context, mobile eye tracking is an innovative approach within teaching effectiveness research to capture teachers' attentional processes while teaching. However, analyzing mobile eye-tracking data by hand is time consuming and still limited. In this paper, we introduce a new approach to enhance the impact of mobile eye tracking by connecting it with computer vision. In mobile eye tracking videos from an educational study using a standardized small group situation, we apply a state-ofthe-art face detector, create face tracklets, and introduce a novel method to cluster faces into the number of identity. Subsequently, teachers' attentional focus is calculated per student during a teaching unit by associating eye tracking fixations and face tracklets. To the best of our knowledge, this is the first work to combine computer vision and mobile eye tracking to model teachers' attention while instructing.