A classroom full of students can be overwhelming for teachers. It is critical that teachers are able to filter and meaningfully interpret the relevant information in this complex scenario. Much of this filtering and interpretation occurs through selective visual perception. Over the last decade, a rapidly growing number of studies in empirical educational research used eye tracking to investigate teachers' selective visual perceptual processes in the classroom. Theoretically, visual attention and perception are often conceptualized as knowledge-based noticing coupled with knowledge-based reasoning about what is visually perceived. Recent eye-tracking studies have shown that experienced teachers, in particular, show improved selective attention of classroom events through faster detection of relevant information and improved monitoring of learning-relevant situations. Researchers agree that a critical driver of selective attention processes is the professional knowledge (declarative, procedural, and metacognitive) of (experienced) teachers, which controls selective perceptual processes in a top-down process. The findings are significant and provide important insights for research on teacher professionalization.
With the outlined focus on research developed in the field of educational psychology and expertise research, either in the laboratory or in real life conditions, this Research Topic aims to address and overcome common issues related to the study of teachers' professional vision and visual expertise using eye-tracking data streams. Some of the most common limitations of previous research are that triangulation with other data (think aloud data, EEG data, emotion etc.) has rarely been performed satisfactorily. In addition, previous results are based on studies with relatively small sample sizes and non-standardized stimuli in the respective experimental setups. Therefore, the goal of this Research Topic is to overcome these limitations through new methodological possibilities, such as the automatic coding of eye tracking data through machine learning approaches or the triangulation of data through data mining techniques. Furthermore, it is unclear to date how teachers' eye movement patterns are related to other learning-relevant outcomes (e.g., students' perceived teaching quality, etc.). Therefore, another goal of this Research Topic is to link eye movement patterns with other teaching-relevant variables (also including questionnaire data) or facets of teachers' professional knowledge.
We encourage researchers to submit original empirical studies, methodological articles, and perspectives that focus on the implementation of new techniques, models, and theories to advance our understanding of teachers' visual perception in the classroom or learning-related settings. We welcome submissions from any academic field with a preference for social science and psychological disciplines either quantitative or qualitative.
Topics to consider may include, but are not limited to:
- Implement machine learning approaches to teacher eye-tracking data streams;
- Triangulation of teacher gaze and verbal reports or other data streams (e.g. EEG, emotion recognition);
- Relationship between teacher gaze models and learning-relevant variables (e.g., perceived teaching quality);
- Relationship between professional knowledge and teacher gaze models;
- Comparison of eye-tracking data from the field (e.g., classroom data) and from the lab (e.g., video vignettes);
- New data collection methods (emotion recognition, web-cam eye-tracking).
A classroom full of students can be overwhelming for teachers. It is critical that teachers are able to filter and meaningfully interpret the relevant information in this complex scenario. Much of this filtering and interpretation occurs through selective visual perception. Over the last decade, a rapidly growing number of studies in empirical educational research used eye tracking to investigate teachers' selective visual perceptual processes in the classroom. Theoretically, visual attention and perception are often conceptualized as knowledge-based noticing coupled with knowledge-based reasoning about what is visually perceived. Recent eye-tracking studies have shown that experienced teachers, in particular, show improved selective attention of classroom events through faster detection of relevant information and improved monitoring of learning-relevant situations. Researchers agree that a critical driver of selective attention processes is the professional knowledge (declarative, procedural, and metacognitive) of (experienced) teachers, which controls selective perceptual processes in a top-down process. The findings are significant and provide important insights for research on teacher professionalization.
With the outlined focus on research developed in the field of educational psychology and expertise research, either in the laboratory or in real life conditions, this Research Topic aims to address and overcome common issues related to the study of teachers' professional vision and visual expertise using eye-tracking data streams. Some of the most common limitations of previous research are that triangulation with other data (think aloud data, EEG data, emotion etc.) has rarely been performed satisfactorily. In addition, previous results are based on studies with relatively small sample sizes and non-standardized stimuli in the respective experimental setups. Therefore, the goal of this Research Topic is to overcome these limitations through new methodological possibilities, such as the automatic coding of eye tracking data through machine learning approaches or the triangulation of data through data mining techniques. Furthermore, it is unclear to date how teachers' eye movement patterns are related to other learning-relevant outcomes (e.g., students' perceived teaching quality, etc.). Therefore, another goal of this Research Topic is to link eye movement patterns with other teaching-relevant variables (also including questionnaire data) or facets of teachers' professional knowledge.
We encourage researchers to submit original empirical studies, methodological articles, and perspectives that focus on the implementation of new techniques, models, and theories to advance our understanding of teachers' visual perception in the classroom or learning-related settings. We welcome submissions from any academic field with a preference for social science and psychological disciplines either quantitative or qualitative.
Topics to consider may include, but are not limited to:
- Implement machine learning approaches to teacher eye-tracking data streams;
- Triangulation of teacher gaze and verbal reports or other data streams (e.g. EEG, emotion recognition);
- Relationship between teacher gaze models and learning-relevant variables (e.g., perceived teaching quality);
- Relationship between professional knowledge and teacher gaze models;
- Comparison of eye-tracking data from the field (e.g., classroom data) and from the lab (e.g., video vignettes);
- New data collection methods (emotion recognition, web-cam eye-tracking).