Eye tracking methodology for studying teacher learning: a review of the research

Beach, Pamela; McConnel, Jen

doi:10.1080/1743727x.2018.1496415

Cited by 39 publications

(27 citation statements)

References 45 publications

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“…Sometimes, digital reaction time tests (e.g., and criteria-based video vignettes (e.g., Bruckmaier et al, 2016;Kaiser et al, 2017) are additionally used for assessment. Moreover, formerly lab-based complex eye-tracking procedures are increasingly deployed via mobile devices in naturalistic classroom settings (see, Beach & McConnel, 2019, for a review). All these methods are nowadays used to assess the quality of teaching (see, Fauth et al, 2020;Praetorius et al, 2018, for an overview).…”

Section: For the Analysis Of Large-scale Studiesmentioning

confidence: 99%

Machine Learning for the Educational Sciences

Hilbert¹,

Coors²,

Kraus³

et al. 2021

Preprint

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Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

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Section: For the Analysis Of Large-scale Studiesmentioning

confidence: 99%

Machine Learning for the Educational Sciences

Hilbert¹,

Coors²,

Kraus³

et al. 2021

Preprint

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show abstract

“…While we know from large amounts of research that the role teachers play for students' learning is key, we also know that not only beginning teachers suffer from underdeveloped professional vision (Sabers et al 1991) but also experienced teachers do not see everything that is happening in the classroom (Nuthall 2005). Still, the field of research on measuring teachers' professional vision directly with eye-tracking is relatively new as was shown by a recent review referencing no more than ten thus far published studies (Beach and McConnel 2019). Wyss et al investigated how teacher educators detected (measured via eye-tracking) and interpreted (measured via verbal reports) critical incidents happening in a classroom as seen on a video recording.…”

Section: Conceptual Innovations-gaining Novel Insights Into Establishmentioning

confidence: 99%

Eye-Tracking in Educational Practice: Investigating Visual Perception Underlying Teaching and Learning in the Classroom

2020

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Classrooms full of pupils can be very overwhelming, both for teachers and students, as well as for their joint interactions. It is thus crucial that both can distil the relevant information in this complex scenario and interpret it appropriately. This distilling and interpreting happen to a large extent via visual perception, which is the core focus of the current Special Issue. Six empirical studies present examples of how to capture visual perception in the complexity of a classroom lesson. These examples open up new avenues that go beyond studying perception in restricted and artificial laboratory scenarios: some using video recordings from authentic lessons to others studying actual classrooms. This movement towards more realistic scenarios allows to study the visual perception in classrooms from new perspectives, namely that of the teachers, the learners, and their interactions. This in turn enables to shed novel light onto well-established theoretical concepts, namely students' engagement during actual lessons, teachers' professional vision while teaching, and establishment of joint attention between teachers and students in a lesson. Additionally, one theoretical contribution provides the very first model of teachers' cognitions during teaching in relation to their visual perception, which in turn will allow future research to move beyond explorations towards hypothesis testing. However, to fully thrive, this field of research has to address two crucial challenges: (i) the heterogeneity of its methodological approaches (e.g., varying age groups, subjects taught, lesson formats) and (ii) the recording and processing of personal data of many people (often minors). Hence, these new approaches bear not only new chances for insights but also new responsibilities for the researchers.

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“…Papers in this Special Issue used eye tracking to study teacher expertise, or "professional vision" of teachers by presenting videos depicting classroom events and student behavior (Seidel et al, this issue; Wyss et al, this issue) or by examining teacher's eye gaze behavior in an actual instructional context (Pouta et al, this issue). Eye tracking has long been utilized to study expert behavior in different domains, such as chess and radiology (Gegenfurtner et al 2011;Reingold and Sheridan 2011), and recently also in teaching (Beach and McConnel 2019) and education (Jarodzka et al 2017). Reingold and Sheridan (2011) suggested that eye movements are sensitive to two particular aspects of expert behavior: superior domain-related perceptual skills and tacit domain-relevant knowledge.…”

Section: Eye Gaze As An Indicator Of Teacher Expertisementioning

confidence: 99%

What Can Eye Movements Tell us about Visual Perception Processes in Classroom Contexts? Commentary on a Special Issue

Kaakinen

2020

Educ Psychol Rev

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In this commentary to the Special Issue of Educational Psychology Review on visual perceptual processes, I tie the empirical studies reported in the issue with previous research in other domains to offer some points to be considered in future studies. First, I will point out to issues related to the operationalization of the theoretical constructs. The empirical papers in this Special Issue use eye tracking to study students’ engagement, teachers’ expertise, and student-teacher interaction. However, it is not always clear how the observed eye movement patterns reflect these theoretical concepts and the underlying psychological processes. Second, I will reflect on the analyses of the eye movement data presented in the papers. The main advantage of the methodology is that it can provide detailed information about the time-course of processing, and to fully engage its potential, it should be complemented with adequate statistical methods. In my view, the papers in this Special Issue provide valuable novel information about the complex processes underlying learning in variable contexts, and offer an excellent starting point for future research.

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Eye tracking methodology for studying teacher learning: a review of the research

Cited by 39 publications

References 45 publications

Machine Learning for the Educational Sciences

Machine Learning for the Educational Sciences

Eye-Tracking in Educational Practice: Investigating Visual Perception Underlying Teaching and Learning in the Classroom

What Can Eye Movements Tell us about Visual Perception Processes in Classroom Contexts? Commentary on a Special Issue

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