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.
The first study which links MET and high-inference assessment has been done by @cite_9 . They used fixation points and manually assigned them to a list of eight standard area of interests (e.g. black board, instructional material, student material, etc.). They investigated the variation of different skills and variables among expert and novice teachers.
@cite_9
1981869448
Classroom observation research and research on teacher expertise are similar in their reliance on observational data with high-inference procedure to assess the quality of instruction. Expertise research usually uses low-inference measures like eye tracking to identify qualitative difference between expert and novice behaviors and cognition. In this study, we used mobile eye-tracking technology to create a low inference quality indicator for the comparison of experienced and student teachers. The distribution of visual fixations on students was measured using Gini coefficients based on the observation of van den Bogert, van Bruggen, Kostons, and Jochems (Teacher and Teacher Education, 37, 208–216, 2014) that expert teachers show better classroom monitoring. Results confirm that student teachers have a higher Gini coefficient than experienced teachers indicating weaker classroom monitoring. However, the Gini coefficient did not correlate in the predicted way with trained observer coding of video footage of the same classrooms using the Classroom Assessment Scoring System (CLASS) (Pianta, Hamre, Haynes, Mintz, & La Paro, 2007) although the mean differences in behavioral management were higher for the experienced teachers as expected. The CLASS dimension Quality of Feedback was significantly related to the Gini coefficient as an interaction with expertise: Only for novice teachers that a high quality of feedback was negatively associated with monitoring of the classroom.