Discipline-based educational research takes the principles of psychology and applies them to specific disciplines to investigate how individuals learn the concepts and skills of that particular discipline. This includes STEM fields such as chemistry, physics, and mathematics, as well as arts and humanities fields such as history. The field of chemistry education research (CER) is focused on how students understand chemistry, identifying the struggles they face when learning chemistry, and trying to improve instruction to make chemistry more accessible.
The main problem that learners of chemistry face is the intangible nature of the discipline. Chemistry focuses on the interactions of atoms and molecules too small to see with the naked eye, and as such, students have no real-world experience with these phenomena. Alex Johnstone suggests that chemistry is difficult to learn because it must be understood on three levels; the submicroscopic (or particulate) level of atoms and molecules, the macroscopic level of everyday phenomena that can actually be observed, and the symbolic level of equations and representations that we use to convey these ideas to one another. Expert chemists can seamlessly integrate these three levels, but novice students struggle to make these connections. Specifically, we frequently find students who are mathematically capable can achieve success in chemistry without ever understanding the concepts, because they are able to use algorithms and heuristics to solve problems without ever connecting their mathematical understanding to its particulate-level causes or real-world macroscopic effects. To combat this, researchers suggested incorporating particulate level diagrams into instruction to help students visualize this submicroscopic level more concretely. This has led to a boom in the use of visualizations in chemistry, including not only static images but also animations and simulations to help improve instruction and students' conceptual understanding.
Because the symbolic level is so prevalent in chemistry, the field has long focused on investigating students' interactions with visualizations in instructional materials including textbooks, online homework systems, and electronic resources such as animations and simulations. Chemistry education researchers also investigate less visually-focused areas, including student problem-solving strategies, conceptual understanding, and the impact of affective domains such as self-efficacy and motivation on student success.
To study these questions, CER frequently borrows research techniques from psychology and other social sciences. In the past, these research questions would be answered through less direct means, including interviews, surveys, and observational protocols or achievement tests. The introduction of eye tracking has allowed more direct, quantitative measurements of student behavior, and has seen wide adoption in CER over the past decade. Eye tracking particularly lends itself to investigating student use of visualizations and instructional materials, but has also been applied to studying topics like problem-solving practices.
One of the earliest uses of eye tracking in the field of CER was my own research into how students view animations of particulate level interactions, as compared to how experts view these same animations. Previous literature, using student achievement tests and interview protocols, had shown that these animations were not improving student achievement in the classroom, despite experts (classroom teachers) encouraging their use to help students understand particle motion and interaction Eye tracking allowed us to investigate where this disconnect came from, and was able to help show classroom instructors that their students were literally not seeing what the experts themselves were seeing—they were focused on entirely the wrong area of the animations. Eye tracking technology allowed us to solve this problem by testing small changes to the animations, like highlighting particles of interest to draw visual attention, and improve student achievement and conceptual understanding.
Since then, eye tracking has been used to investigate a number of other visualizations in the chemistry classroom. A recent project by Herrington et al looked at how students interact with simulations, which allow them to manipulate variables and observe the particulate level and macroscopic outcomes of these changes. This study demonstrated that students without scaffolding and instruction do not use the particulate level resources provided to them and focus instead on answering questions using prior knowledge and algorithmic thinking.
Stieff et al looked at students' use of multi-representational displays, showing split attention between various visualizations, as well as a student preference for visuo-spatial representations over more mathematical ones. This is complemented by the work of Williamson et al, who showed that students presented with multiple representations to solve problems will use only the most familiar, ignoring more useful representations in favor of simpler representations.
In terms of investigating student problem solving, many researchers have also used eye tracking to see how students interact with visual data, such as chemical spectra. Cullipher et al showed that novices and experts read these spectra differently, allowing suggestions for the classroom instructor on how to best teach this crucial skill to budding chemists. Cortes et al similarly investigated how students read images containing complicated biochemical pathways and how they find information in these pathways to answer questions. This helps us understand problem-solving behavior in a way that interviews do not, and allow us to see in real-time what users are doing.
More generally, eye tracking has been used to investigate how students read and respond to word problems and multiple-choice problems. While eye tracking has been widely applied to investigate reading behavior in other fields, these studies give insights to classroom instructors and assessment designers about how to best formulate assessment items to test student knowledge in chemistry without overloading students' cognitive resources.
Overall, eye tracking has proved immensely beneficial to the field of chemistry education research by quantifying student behavior in a way that previous methods did not allow. This has helped us improve how we design instructional materials such as animations and simulations, as well as how we teach students to solve problems using visualizations like spectra and biochemical pathways. While DBER research is incredibly applied, the results from these studies are unique to the fields under study and speak more clearly to the practitioners of these fields. Eye tracking has the advantage of producing quantitative data, speaking the language of those in STEM fields, rather than qualitative analysis. These quantitative results are more familiar to STEM faculty, thus lowering the barrier to their acceptance of the findings. For DBER researchers interested in student use of educational materials, particularly those in visually demanding fields such as chemistry, eye tracking is uniquely suited to provide data to help improve instruction. Those in DBER that wish to know more about how to use this useful tool should consult a recent overview.
Written by
Dr. Jessica VandenPlas
Associate Professor of Chemistry, Grand Valley State University
Dr. Jessica VandenPlas is an associate professor of chemistry at Grand Valley State University. She received a Ph.D. in educational psychology from the Catholic University of America in 2008, after completing an MS in Forensic Science at the George Washington University, and a BS in biochemistry from the University of Wisconsin, Madison. Her research is focused in the area of chemistry education, and uses educational and psychological methods to investigate student learning in chemistry. Current research is focused on using eye tracking techniques to examine student problem solving and representational competence in chemistry, as well as the use of technology in the classroom.
Dr. Jessica VandenPlas introduces the unique research challenges faced by chemistry education researchers, highlighting the role that eye tracking can play in addressing these difficulties. Examples is given from her own research program, including student use of multiple representations in organic chemistry and the use of animations and simulations in general chemistry.
Dr. Stefan Küchemann demonstrates two scenarios which aim to reveal the visual attention distribution and interactions of students in real-world learning environment in physics with a high ecological validity.
Dr. Stefan Küchemann shows how mobile eye tracking extends the range of possible eye tracking applications in physics education research and demonstrate underlying visual strategies during the generation process of visual representations and during experimentation.
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