Abstract
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.
Highlights
In day-to-day teaching, teachers have to continuously monitor a classroom full of students, respond to questions, observe students’ learning progress, and assess how students react to their instructions—briefly, teaching is characterized by multi-dimensionality, immediacy, and simultaneity (Doyle, 2006)
The present study extends our previous work on the same dataset and experimental setup (Seidel et al, 2020), in which expert and novice teachers were asked to observe a video clip of an authentic teaching situation and to assess five students based on their underlying learning-relevant student characteristics
We calculated a set of intra-individual LDss as well as a set of inter-individual LDss
Summary
In day-to-day teaching, teachers have to continuously monitor a classroom full of students, respond to questions, observe students’ learning progress, and assess how students react to their instructions—briefly, teaching is characterized by multi-dimensionality, immediacy, and simultaneity (Doyle, 2006). There has been a growing interest in teachers’ eye-tracking data, in terms of the number or duration of fixations (van den Bogert et al, 2014; Wolff et al, 2016; McIntyre et al, 2017; Stürmer et al, 2017; Seidel et al, 2020; Wyss et al, 2020) Such metrics were used in the above studies, for example, to demonstrate that expert teachers were able to process visual information more quickly than others, or that novice teachers focused more often on non-relevant classroom events. Accurate teacher assessments are crucial to adapting their pedagogical actions to students’ individual needs and to support students’ individual learning progress (Klieme et al, 2009)
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