Identifying Expert and Novice Visual Scanpath Patterns and Their Relationship to Assessing Learning-Relevant Student Characteristics

The paper addresses cognitive processes during a teacher's professional task of assessing learning-relevant student characteristics. We explore how eye-movement patterns (scanpaths) differ across expert and novice teachers during an assessment situation. In an eye-tracking experiment, participants watched an authentic video of a classroom lesson and were subsequently asked to assess five different students. Instead of using typically reported averaged gaze data (e.g., number of fixations), we used gaze patterns as an indicator for visual behavior. We extracted scanpath patterns, compared them qualitatively (common sub-pattern) and quantitatively (scanpath entropy) between experts and novices, and related teachers' visual behavior to their assessment competence. Results show that teachers' scanpaths were idiosyncratic and more similar to teachers of the same expertise group. Moreover, experts monitored all target students more regularly and made recurring scans to re-adjust their assessment. Lastly, this behavior was quantified using Shannon's entropy score. Results indicate that experts' scanpaths were more complex, involved more frequent revisits of all students, and that experts transferred their attention between all students with equal probability. Experts' visual behavior was also statistically related to higher judgment accuracy.