Learning, to a certain extent, is instinctive. The way we absorb information, assimilate it, and file it in our brain has long been the subject of scientific interest, with researchers dedicating careers to deciphering the inner workings of a brain being taught. Through EEG and other lab-based techniques we have been able to read patterns of the brain, but with eye tracking, we have access to additional, implicit insights without relying on invasive methods. We can observe a child’s cognitive load, understand how academically gifted students process information, and invent new ways of teaching that align with a richer understanding of the learning brain. Here are some of the top ways in which eye tracking is being used in educational research to further our understanding of the learning process.
You weren’t aware of this when you were a baby, but when your parents pointed to an apple and said the word, a lot of things were going on in your brain. Firstly, you were receiving the sounds, then you were mapping the journey between the label and the object in question. Each time the word was repeated, it forged an even stronger association in your brain, but there was something else helping the process along the way. The shared focus of you and your parent, the apple, meant that you knew she wasn’t talking about your doll, your toy car, or the cat that just wandered in. The gaze attention was on the apple, and so you learnt the word with ease. This phenomenon is known as joint attention, and it is a fundamental principle of how we learn as children.
Researchers in the field of education have known about this concept for a while, but with the use of eye tracking, our understanding of the infant learning process has expanded greatly. Not only are we able to assess to what extent the idea of joint attention is at play in the early acquiring of language and understanding of communication, but we are also able to navigate situations in which joint attention is necessarily absent or severely restricted.
Maintaining the attention of 30 children in a classroom long enough to teach the material is a difficult enough task, and that was before widespread remote learning. Throughout the course of the Covid-19 pandemic, schools and learning institutions across the world have been forced to shutter their windows and teach through new digital mediums. Where you would usually have a teacher at the front of class drawing attention from the students to the blackboard, the pupils are instead left to intuit through video calls where the teacher is looking and to what materials they are referring when they speak. This may seem like a minor inconvenience, but it subverts a crucial foundation of the way humans learn.
Using eye tracking, education researchers are finding ways to redesign learning practices and resources to flourish in the age of distance learning, as well as for the inevitable return to the classroom. While children continue to learn from home, adapted presentation tools and online tutorials that show the teacher’s gaze pattern could help with the absence of joint attention scenarios. In video classes, putting more of a focus on the teacher as opposed to the material has also been shown to trigger a joint attention learning moment, boosting the efficacy of the teaching session. Eye tracking insights are continuing to carve the way for these concepts to be imagined and tested in the real world.
Even before widespread distance learning, eye tracking has been used to assess how the learning environment for students can be optimized. Since research began into teacher’s “professional vision” nearly 20 years ago, we’ve learnt how veterans of the education profession can read visual cues from authentic classroom experiences, extracting real data from effective teaching techniques.
Since eye tracking has been introduced into the field of education research, a more detailed picture of these teaching methods has been drawn. By analyzing gaze patterns in teachers, researchers can learn how they scan for potential disturbances, pick out struggling individuals, and keep track of how the content is being received across the classroom. Education researcher Halszka Jarodzka tells how “in this interactive, dynamic environment, teachers’ visual perception of the classroom plays a key role in classroom management and teaching” (Jarodzka: 2020). These eye tracking studies into the attention of experienced teachers can help newer educators ensure the students in their class are focused and receptive, boosting their chances of performing to the best of their ability.
This particular avenue of study has a way to go, and Jarodzka goes on to explain how “more mobile eye tracking research would allow us to understand how teachers’ visual perception comes to play in the very act of teaching” (Jarodzka:2020). This is an exciting perspective, and it allows us to imagine a new field of psychological study in which the very act of teaching can be isolated, examined, and understood!
Teachers are not developmental scientists, but historically they have been expected to make complex judgements about reading and comprehension ability that can have lasting effects on a child’s learning. When these decisions are made accurately and early, children can be nurtured with bespoke learning programs that help them to leave school at the highest reading level possible. On the other hand, delayed determinations rob children of the chance to get a head start on their learning.
One solution to mitigate the danger of this is using eye tracking to measure reading patterns. Where you would once be watching a child closely to understand the speed and ease of their reading, eye tracking allows you to isolate specific areas of cognitive load, precise hesitations, and particular word difficulties. In this way, it is now possible to differentiate between a child more accurately with real and repeated difficulties reading complete sentences and another child who only struggled on an isolated term or who got distracted during the assessment.
Eye tracking technology is empowering these developments, and solutions like Tobii’s new reading specific metrics and automatic analysis tools like Tobii Pro Lab, are inevitably going to have a part to play in the field going forward.
We’ve all been in a situation in which a complex text has severely slowed our ability to internalize the information. Jargonistic words, complex phraseology, and industry vernacular all play their part in an inefficient reading process, keeping certain individuals from becoming truly inspired. This is all too common in academia, but luckily there are researchers investigating how scientific papers can be made easier to understand without watering down the content.
One exciting new example is around that of text summary augmentation, a process by which a reader’s gaze can be tracked and assessed for cognitive load. When higher load is recorded over a particular word or scientific term, concepted software from the German Research Center for Artificial Intelligence will consider this to be a moment of hesitation and will introduce a definition for the term in a way that is helpful and unobtrusive. Without leaving the screen and searching for the meaning themselves, the reader of the paper can understand the term, its context in the sentence, and carry-on reading without breaking their concentration.
What the team behind this software is pursuing are intelligent reading solutions that remove the secondary responsibilities of the reader, allowing them to focus on the information itself. Their algorithm means that periods of “low confidence”(Vadiraja, Dengel, Ishimaru: 2021) can be identified, and the most appropriate helping-hand can be extended to nudge them in the right direction.
We need only imagine the potential applications for software like this in a variety of situations: from early developmental reading resources to aides for people with learning disabilities, algorithms that understand a reading struggle and respond in real time have endless real-world applications.
Students of chemistry face a unique obstacle when first coming to learn the subject in detail. As opposed to some other scientific disciplines, chemistry is always intangible. It is harder to internalize because the students have limited authentic, real-world experience of the phenomena being explored. To be mastered, chemistry needs to be understood on the sub-microscopic, macroscopic, and symbolic level; while expert chemists can weave these together simultaneously, novices find it a high wall to climb.
Naturally, this has led to a boom in the use of visualizations in the field. Consisting of static images and crude animations, these tools have done the best they can to transmit the complex concepts that chemistry provides. Eye tracking is broadening our general understanding of how experts and beginners alike absorb visual representations of chemistry events, allowing researchers like Jessica R. Vandenplas to understand why new students found them useless despite experts consistently recommending their usage. In Vandenplas’ research, eye tracking data showed a disconnect between what the experts saw when engaging with the material, and what the novices saw. This allowed the gap to be bridged with further explanation on behalf of the teacher that brought their attention to different areas of the visual resource.
Animation courtesy of the VisChem Project.
Another study by Herrington and colleagues (2008) analyzed gaze data of students while they engaged with simulations that allow them to manipulate microscopic variables and observe the outcomes. This showed that students without expert guidance on-hand would rely on their own limited knowledge and algorithmic thinking as opposed to the resources provided to them. In this situation, the insights into how beginners' onboard information allowed them to alter teaching practices to accelerate their rate of learning. Teachers are also empowered to alter their materials so that they are optimized for the ways in which chemistry students learn the subject.
When it comes to modern software development techniques, code reviews are an essential part of the process. Quality assurance in code is arguably just as important as the writing itself and picking out errors early on can save countless money and man hours later on. That’s why it’s so crucial to make sure new code writers are trained in the best way possible.
Educational researchers in Regensburg, Germany are beginning to use eye trackers to visualize the ways in which experienced coders scan for errors in comparison to those relatively new to the field. The aim was to learn if there was a difference in reviewing techniques between new and experienced coders, as well as how these insights could be applied to teach a more effective review strategy for future generations of coders. Nick Schor, the lead researcher in this study, concluded that “advanced and expert programmers performed significantly better in case of error detection and the eye tracking data implies a more efficient reviewing strategy” (Schor et al: 2020).
While code reviews are aided these days by software systems that remove a lot of the grunt work, the process still requires a large portion of manual effort and human decisions at critical moments. What eye tracking enables is an understanding of how these decisions are made, what information informs the reviewer, and how the reviewer theorizes ways in which they can simplify the code itself. In an increasingly technological society, critical insights into existing coding practices are going to prove invaluable to companies looking to get ahead.
Gaining a richer understanding of how different individuals learn harbors possibilities researchers haven’t yet even imagined. Right now, eye tracking is leading the way in decoding thought processes that can’t be explained or rationalized by the subject themselves, helping us to understand some of the most instinctive and natural parts of learning behavior. This is especially true when it comes to analyzing the behavior of young children, with eye tracking insights giving us privileged access to learning habits that would be otherwise unobtainable until a later stage of communication development. But whether it is young children at the beginning of their learning journey, or adults at the end of it, eye tracking offers us a backstage pass into the mind while it learns.
Visit our research pages to learn more about how eye tracking is being used across the fields of education and reading research.
Vandenplas, J. R. (2008). Animations in chemistry learning: Effect of expertise and other characteristics. The Catholic University of America.
Jarodzka, H., Skuballa, I. & Gruber, H. (2021). Eye-Tracking in Educational Practice: Investigating Visual Perception Underlying Teaching and Learning in the Classroom. Educ Psychol Rev 33, 1–10.
Vadiraja, P. Dengel, A. Ishimaru, S. (2021). Text Summary Augmentation for Intelligent Reading Assistant. In Augmented Humans International Conference (AHs ’21), February 22–24, 2021, Rovaniemi, Finland. ACM, New York, NY, USA, 4 pages.
Hauser, Florian & Schreistter, Stefan & Reuter, Rebecca & Mottok, Jurgen & Gruber, Hans & Holmqvist, Kenneth & Schorr, Nick. (2020). Code Reviews in C++: Preliminary Results from an Eye Tracking Study. 1-5. 10.1145/3379156.3391980.
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