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.
McIntyre and Foulsham @cite_36 did the analysis of teachers' expertise between two cultures, in the UK and Hong Kong among 40 secondary school teachers (20 experts, 20 novices) using scanpath analysis. Scanpath is repetitive sequence of saccades and fixations, idiosyncratic to a particular subject [person] and to a particular target pattern''.
@cite_36
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Humans are born to learn by understanding where adults look. This is likely to extend into the classroom, making teacher gaze an important topic for study. Expert teacher gaze has mainly been investigated in the laboratory, and has focused mostly on one cognitive process: teacher attentional (i.e., information-seeking) gaze. No known research has made direct cultural comparisons of teacher gaze or successfully found expert–novice differences outside Western settings. Accordingly, we conducted a real-world study of expert teacher gaze across two cultural settings, exploring communicative (i.e., information-giving) as well as attentional gaze. Forty secondary school teachers wore eye-tracking glasses, with 20 teachers (10 expert; 10 novice) from the UK and 20 teachers (10 expert; 10 novice) from Hong Kong. We used a novel eye-tracking scanpath analysis to ascertain the importance of expertise and culture, individually and as a combination. Attentional teacher scanpaths were significantly more similar within than across expertise and expertise + culture sub-groups; communicative scanpaths were significantly more similar within than across expertise and culture. Detailed analysis suggests that (1) expert teachers refer back to students constantly through focused gaze during both attentional and communicative gaze and that (2) expert teachers in Hong Kong scan students more than experts do in the UK. Visual expertise has been explored in numerous professions, but research on teachers’ vision remains limited. Teachers’ visual expertise is an important professional skill, particularly the ability to simultaneously perceive and interpret classroom situations for effective classroom management. This skill is complex and relies on an awareness of classroom events. Using eye tracking measurements and verbal think aloud, we investigated differences in how expert and novice teachers perceive problematic classroom scenes. Sixty-seven teachers participated, 35 experienced secondary school teachers (experts) and 32 teachers-in-training (novices). Participants viewed videos of authentic lessons and their eye movements were recorded as they verbalized thoughts about what they had seen in the lesson and how it was relevant to classroom management. Two different types of videos were viewed: lesson fragments showing (1) multiple events depicting disengaged students with no overt disruptions and (2) multiple events that included a prominent disruptive event affecting the class. Analysis of eye movements showed that novices’ viewing was more dispersed whereas experts’ was more focused. Irrespective of the video type, expert teachers focused their attention on areas where relevant information was available, while novice teachers’ attention was more scattered across the classroom. Experts’ perception appears to be more knowledge-driven whereas novices’ appears more image-driven. Experts monitored more areas than novices, while novices skipped more areas than experts. Word usage also differed, showing that expertise was associated with a higher frequency of words referencing cognition, perception, and events than novices. 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. This work addresses the challenging problem of simultaneously segmenting multiple anatomical structures in highly varied CT scans. We propose the entangled decision forest (EDF) as a new discriminative classifier which augments the state of the art decision forest, resulting in higher prediction accuracy and shortened decision time. Our main contribution is two-fold. First, we propose entangling the binary tests applied at each tree node in the forest, such that the test result can depend on the result of tests applied earlier in the same tree and at image points offset from the voxel to be classified. This is demonstrated to improve accuracy and capture long-range semantic context. Second, during training, we propose injecting randomness in a guided way, in which node feature types and parameters are randomly drawn from a learned (non-uniform) distribution. This further improves classification accuracy. We assess our probabilistic anatomy segmentation technique using a labeled database of CT image volumes of 250 different patients from various scan protocols and scanner vendors. In each volume, 12 anatomical structures have been manually segmented. The database comprises highly varied body shapes and sizes, a wide array of pathologies, scan resolutions, and diverse contrast agents. Quantitative comparisons with state of the art algorithms demonstrate both superior test accuracy and computational efficiency.