A modern approach to improving education uses the components of experimental scientific research practices based on objective data, dissemination of results, and the use of modern technologies. STEM education research is maturing and new tools and analysis techniques become available. As one example, eye tracking, the recording of persons’ eye movements, has been growing in popularity as it enables researchers to study learning materials’ effectiveness, problem solving, and even students’ approaches during experimentation. Eye movements, as captured using eye tracking, can reveal information about a student's attention and cognition on a process level, going well beyond classical product-based assessment techniques such as questionnaires or tests. At the same time, eye tracking as a method has the potential to circumvent certain issues that go along with other methods connected to process data such as thinking aloud interviews. Process-based information on learning can be very powerful in informing both practice and policy at the national and international levels. As a result of this emerging field, study designs and analytic techniques have been tested and established to investigate students’ visual attention, their cognitive (and affective) processes, and their relation to learning outcomes. When incorporating eye tracking to the research portfolio, researchers must make decisions about the usefulness and appropriateness for particular studies, taking into account the limitations of the method.
The goal of this Research Topic is to provide a set of research articles that presents empirical or methodological eye-tracking studies in STEM education research. We invite manuscripts that use standard methods of eye tracking in education research, as well as those that use novel analytical techniques to provide new insights into the data; that is, for example, machine learning or deep learning approaches.
We welcome papers that focus on eye tracking that are of interest to the STEM education research community and/or that could be used to advance STEM education research. We encourage article submissions from collaborations between STEM and non-STEM education researchers.
This article collection will provide for articles covering (but not limited to) the following:
- Assessment of student’s visual attention and/or cognitive processes during learning, problem solving, and experimental work in STEM
- Investigations of the method itself, e.g., by comparing it to other research methods in STEM
Exploiting visual attention measures for adaptive learning and virtual learning environments in STEM
- Investigating the progression of visual attention and /or cognitive processes over time as a result of teaching interventions
- Comparing visual attention of student groups, such as discipline experts vs. nonexperts
A modern approach to improving education uses the components of experimental scientific research practices based on objective data, dissemination of results, and the use of modern technologies. STEM education research is maturing and new tools and analysis techniques become available. As one example, eye tracking, the recording of persons’ eye movements, has been growing in popularity as it enables researchers to study learning materials’ effectiveness, problem solving, and even students’ approaches during experimentation. Eye movements, as captured using eye tracking, can reveal information about a student's attention and cognition on a process level, going well beyond classical product-based assessment techniques such as questionnaires or tests. At the same time, eye tracking as a method has the potential to circumvent certain issues that go along with other methods connected to process data such as thinking aloud interviews. Process-based information on learning can be very powerful in informing both practice and policy at the national and international levels. As a result of this emerging field, study designs and analytic techniques have been tested and established to investigate students’ visual attention, their cognitive (and affective) processes, and their relation to learning outcomes. When incorporating eye tracking to the research portfolio, researchers must make decisions about the usefulness and appropriateness for particular studies, taking into account the limitations of the method.
The goal of this Research Topic is to provide a set of research articles that presents empirical or methodological eye-tracking studies in STEM education research. We invite manuscripts that use standard methods of eye tracking in education research, as well as those that use novel analytical techniques to provide new insights into the data; that is, for example, machine learning or deep learning approaches.
We welcome papers that focus on eye tracking that are of interest to the STEM education research community and/or that could be used to advance STEM education research. We encourage article submissions from collaborations between STEM and non-STEM education researchers.
This article collection will provide for articles covering (but not limited to) the following:
- Assessment of student’s visual attention and/or cognitive processes during learning, problem solving, and experimental work in STEM
- Investigations of the method itself, e.g., by comparing it to other research methods in STEM
Exploiting visual attention measures for adaptive learning and virtual learning environments in STEM
- Investigating the progression of visual attention and /or cognitive processes over time as a result of teaching interventions
- Comparing visual attention of student groups, such as discipline experts vs. nonexperts
