Eye Tracking in Chemistry Education Research: Study Logistics - ACS

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Chapter 2

Eye Tracking in Chemistry Education Research: Study Logistics Sarah J. R. Hansen*,1 and Jessica R. VandenPlas2 1Department

of Chemistry, Columbia University, New York, New York 10027, United States 2Department of Chemistry, Grand Valley State, Allendale, Michigan 49401, United States *E-mail: [email protected].

Eye-tracking studies for research purposes have unique characteristics that warrant special consideration when setting up, planning, and obtaining Institutional Review Board approval (IRB). This chapter will discuss necessary considerations when seeking approval for an eye-tracking study in chemistry education research and navigating the institutional context for human subject research for discipline-based educational research (DBER). Additionally, we include practical considerations such as using a shared eye tracker, exporting data, combining data from different systems, creating stimuli midtrial, and/or the use of pauses or triggers.

© 2018 American Chemical Society VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

Introduction Eye tracking holds significant potential to complement chemistry education research studies involving visual stimuli (1–6). Logistical considerations arise when combining eye tracking with chemistry education studies, particularly when eye-tracking research is new for a particular department or institution. Designing a research space and planning a research study are both daunting tasks for the new researcher and will be discussed along with additional study considerations, including the use of shared eye trackers, creating stimuli midtrial, and the use of pauses or triggers during eye tracking. Finally, institutional review boards (IRBs) or equivalent bodies are now almost universally charged with reviewing research studies involving human subjects to ensure ethical treatment of the participants and the research data. Because the collection of physiological data involves different considerations than more traditional chemistry education research studies, this chapter outlines IRB considerations for chemistry education researchers working with an eye-tracking system.

Designing a Research Space Selecting an Eye Tracker The first question any researcher must answer when they begin eye-tracking research is what type of eye-tracking system they will use and how to gain access to this system (by borrowing, renting, building, or purchasing a system). While the technology itself is fairly consistent among brands and between models these days, the physical configurations of these systems vary greatly. Some examples of these systems are shown in Figure 1. In order to select an appropriate system, the researcher must identify the basic types of research they wish to conduct and the demands of this research. For example, researchers conducting studies on student behavior in the laboratory will derive no benefit from a screen-based tracker and will likely need to use a headmounted tracker with an integrated scene camera (which operates independently of the eye-tracking camera(s) to capture video of the environment around the user) so that the user is able to interact with real-world objects. Researchers conducting studies on participant interaction with audio-visual material, such as textbooks, images, or animations, on the other hand, will likely benefit from the stability and ease of analysis that comes from using a screen-based tracker. Even among screen-based trackers, however, there are variations in the quality of data collected (based on sampling frequency and accuracy of the tracking hardware and software) that must be considered. Researchers conducting highly sensitive research, including research on pupillometry (see Chapter 7) or research where very small eye movements could be significant, including some reading research, may require systems with higher sampling rates or systems which offer some stability to the user (see Figure 1). Once a researcher has identified the physical configuration of the system that would best work for their research, they will likely 12 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

find only one or two brands that offer this physical configuration, thus narrowing the search significantly.

Figure 1. Two example eye-tracking systems. The first (left), is a screen-based SMI REDn system, which uses a remote IR light source and sensor attached directly to the computer, such that the system is not in direct contact with the participant. The second two images are of a Grinbath system. This head-mounted system rests on the participant like a hat, with the IR light source and sensor bending around the head. Setting up an Eye-Tracking Lab Depending on the type of eye tracker you are using, there may be some flexibility in the location that can be used for research. A shared eye tracker, particularly in another department, may already be installed, and you will need to consider how to guide your participants to the location and schedule trials in a potentially shared space. It is important to check that settings have not changed between sessions in order to guard against data compromise caused by a system crash or another researcher’s activity on the system you share. If your eye tracker is portable you may want to consider a fixed location where you can control the ambient light and allow your participant to be comfortable and uninterrupted. In terms of lighting concerns, researchers should consider a location with consistent lighting between sessions, and without bright light shining directly on the tracker or in the participant’s face. Consult the set-up manual for your particular system for suggestions on optimal lighting, participant distance from the screen, chair height, etc. Additionally, make-up (such as heavy mascara), glasses, contact lenses, and eye color or shape may also impact eye-tracking data collection (7). Chapter 1 discusses study design and set-up considerations.

Designing an Eye-Tracking Study Appropriate Research Questions Eye-tracking data has the potential to provide insight into participant behavior beyond traditional interview or observational methods. For example, if a student is viewing an animation of a redox reaction involving copper with copper ions floating in solution but does not mention the copper ions as being relevant during a subsequent interview, we may wonder if they even saw these atoms, or if the atoms were seen but not considered important (8). Similarly, a participant using 13 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

a simulation may direct their attention to the portions of the screen necessary to enter their answer; in this case, knowing where they look provides a more complete understanding of their engagement with the task (9). Eye tracking can tell you if visual attention is being allocated to a particular feature of the image (Figure 2). Similarly, eye tracking can give insight into the order in which an individual interacts with the simulation including when they read the balanced equation relative to answering a particular question (Figure 2). Research questions that deal with attentional metrics such as these (duration or number of views in a particular region, the order of viewing particular regions, etc.) are ideally suited for eye-tracking research. When designing a study, it is important to identify the type of data that will best address your research questions—eye tracking alone may not be appropriate or sufficient to address all research questions. Questions about what behaviors participants demonstrate (including what they pay attention to when completing a task) can be addressed via eye tracking. However, questions about why participants demonstrate certain behaviors may best be answered through interviews or thinkaloud protocol analysis instead of, or in addition to, eye-tracking data (Chapter 7). At the same time, eye-tracking data can also provide a different view of the participant’s experience that can help supplement or complement these additional data sources. For example, participants who describe a reaction and successfully answer questions about that reaction may meet our standard for understanding the reaction, but their viewing pattern may reveal large, chemically relevant portions of the image that were not attended to. This may indicate gaps in understanding that would not be detected by traditional interview methods. Coupling eye tracking with additional data sources, then, can provide a rich and often complex view of the participant’s experience (10–12).

Figure 2. Different participants playing the same simulation. Each circle is a fixation (the size of the circle indicates the length of the fixation). The participant on the left did not view the submicroscopic images while the participant on the right did not look at the balanced equation (9). (see color insert)

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Working with Human Subjects Before beginning any data collection, it is crucial to seek approval for working with human subjects, generally through an institutional review board (IRB). A good starting point for chemistry education researchers is Christopher Bauer’s chapter in a previous ACS symposium series book, Tools of Chemistry Education Research (13). In addition to viewing data, some eye-tracking systems can collect a video of the participant, audio, or click data. Collecting only the data needed for the research study can streamline the IRB process and minimize the personally identifiable data collected. IRB processes are extremely institution-specific, but it is always a good idea to contact your office of Human Research Protection (or equivalent campus resource) early for guidance if you intend to publish or share your findings at a conference. It may also be helpful to discuss the timeline and process with colleagues who have already obtained IRB approval.

Data Collection and Consent Forms The process of creating consent forms, designing a research protocol, and collecting, storing, and analyzing data requires careful planning. If the eye tracker records an image of the participant but this information is not needed for the analysis, then covering or disconnecting the front camera prevents extra (identifiable) data collection and may make the IRB approval process easier. If you are analyzing participant gestures or interactions with a molecular model, then the front camera may be needed and the merit of this additional data is clearly justified in your protocol. Reflecting on the utility of audio data when designing the study may be useful; if participants can draw or organize their responses using graphic organizers, it may decrease the need for transcription. Some institutions may require additional scrutiny and consent if transcriptions of the audio are required for the research project. Eye-tracking data collection may increase the participant’s discomfort (14, 15). Being very specific about how the eye tracker collects the data may help. Does the eye tracker touch the participant? If it rests on the head or face, is it similar to how a hat or glasses would rest (See Figure 1)? Clarifying how the eye tracker interacts similarly to an item the participant is familiar with can provide clarity and minimize confusion with both the IRB and potential research participants. For example, your participants may be familiar with video game systems that track movement using IR sensors. Is the participant able to look away during the session? Will they be given breaks if needed or does the trial need to be completed during a single sitting? Additionally, some institutions request clarification about the IR beam used in the eye tracker, and it may be necessary to include specific safety warnings found in the manual for a particular eye tracker, such as warnings about photosensitive epilepsy (16) or safety guidelines the eye tracker meets. You may want to include the manual for your eye tracker with your IRB submission. For more discussion of stimuli design, look-away behavior, and eye fatigue, see Chapter 3. 15 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

Eye-tracking protocols will be classified differently by different institutions. It may be worth reaching out to your particular IRB in order to determine if they have approved eye-tracking studies before, and if so, which section of the Federal Policy for the Protection of Human Subjects these studies are normally approved under. Most commonly, eye tracking falls under exempt or expedited review categories, including 45 CFR 46.110a, category 4 (collection of data through noninvasive procedures) (17). Study Populations Pre-screening participants may be useful if you need to exclude some volunteers. For example, your institution may require you to screen for and exclude participants who are epileptic due to the IR light used to track viewing. Other populations you may want to exclude include: participants with specific medical conditions (color blindness, cataracts, etc.), those under 18 who would require parental consent, individuals requiring tinted or specialized glasses that might interfere with data collection, and volunteers who are currently your own students. Other factors such as stress, time of day, eye strain, and cognitive impairment can also impact the reliability of eye-tracking results. Chapter 3 includes a discussion of participant considerations with regard to stimuli design. These conditions may be more difficult to manage but should be noted. Consult the manual for your specific eye tracker regarding the limitations of the system you are using. It is important to note that prescreening your volunteers alone may require a separate consent form. Shared Eye Trackers If you are using an eye tracker in another department or another institution you may need additional IRB approval. This can also be true when recruiting participants from a different institution. Although institutions may be closely affiliated, each will have their own IRB and will require different levels of protocol approval. Some eye trackers can be moved easily between study locations (e.g. glasses or laptops) while others require you to bring the participant to a specific site. When working in another laboratory or institution you may need a letter stating you have permission from that lab’s PI to collect data there. Similarly, co-registration (collecting eye tracking and other biometric data together) has its own considerations, which are discussed in Chapter 7.

Collecting Data Practical Considerations During the data collection, you need to take into account the task you will ask participants to complete. If this task involves the participant making some sort of output, such as problem-solving or diagram generation, you must consider how this data will be collected. It is important to think about where the participant’s eye will be during each task and if shifting tasks will require you to recalibrate the 16 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

eye tracker. If your system loses calibration when participants look away in order to record their output, you may want to have them speak their responses rather than writing them. Depending on the type of additional data you need, a writing or drawing device (e.g. a Waacom tablet or iPad with a screen and audio recording software such as Vittle) may also be appropriate. Figure 3 shows data collected during an eyetracking session using the Vittle software (18). This software allows the researcher to upload images for the participant to edit. All edits are collected (anything written or drawn is then erased and edited) along with the audio at that point in the trial. The data is saved as an exportable movie file. Another option for allowing the collection of user outputs is a head-mounted eye tracker with a whiteboard.

Figure 3. A participant’s image (drawn on a tablet) in response to a drawing prompt. Audio data were collected along with eye-tracking data when the participant viewed a still image (on a second screen) (18). The analysis you use will need to take into account how the experiment and stimuli are structured. For example, participants who are able to look away from the screen will not have their eyes tracked during this time. Analyzing your data into terms of percentage fixation time or total fixation count can account for participants who look away from the eye tracker. This approach can also work when participants are allowed to view the stimuli for different lengths of time. Another consideration is who collects the data. Some researchers find that undergraduate research assistants interviewing other undergraduates make participants feel more comfortable. In this case, you will want to consider how you train the undergraduate research assistants and what the back-up plan is if the equipment experiences technical difficulties. Dry runs with the research assistants collecting data from the other researchers on the study is one way to practice and flush out sticking points. This is especially important if they need to process information during the trial and make edits to the experimental protocol while the participant is waiting. Making sure to check that the calibration is accurate before proceeding with the study is vital. If the trial has multiple separate experiment protocols using the eye tracker, you will need to consider if each requires a separate calibration and what level of calibration the eye tracker requires between participants. Older systems may benefit from a reboot between participants to clear working memory. 17 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

Pauses and Triggers It may be useful to include a pause frame between multiple stimuli that provides a moment for the researcher to check in with the participant before moving on. The researcher can then advance the experiment manually rather than allowing it to proceed in an automatic progression. If your system allows for trigger AOIs (Figure 4), adding them requires all participants to view a defined area of the screen for a continuous pre-set length of time before the experiment moves forward. This is one way to ensure the participants begin viewing the stimuli within that defined area on the screen. This may be particularly important for studies involving pupillometry, where establishing a baseline pupil diameter between tasks is important (see Chapter 7), or for tasks where response times are collected and all participants begin the task by viewing the same on-screen location. When using trigger AOIs, it is important to remember the viewing will need to be continuous. While one second seems short, it may be a long time for your participant to view continuously. A setting of 500 ms may be more attainable due to natural saccades. Similarly, the size of the AOI needs to be considered. A discussion of eye-tracking stimuli, including the use of triggers, can be found in Chapter 3.

Figure 4. A trigger AOI shown in yellow/gray. The eye-tracking experiment does not advance to the next image until fixations are detected in this area for a set amount of time. Creating Stimuli Midtrial Eye tracking offers researchers another unique opportunity: the ability to provide visual feedback to participants on their own viewing patterns. Visual feedback has been used to develop not only eye-controlled interfaces, but also gaze-dependent displays, which alter the stimulus presented based on a user’s viewing pattern. These modified displays can be achieved through the use of an eye-tracking system that is designed to respond directly to the participant’s eye movements (e.g. the Tobii 4C system or the use of programs like MATLAB (19)). For example, if a researcher wishes to study the impact of visual feedback on a 18 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

participant’s fixations on chemically relevant features in a simulation, the system can create a gaze-dependent display after each user interaction with the simulation. This requires a short break in the study while the researcher exports an inverse gaze opacity image and then inserts this image into a second eye-tracking experiment. A standard gaze opacity image shows only the areas the participant viewed with the rest of the screen blacked out; an inverse gaze opacity image instead blacks out where the participant looked, leaving the rest of the stimuli uncovered. Figure 5 shows how an inverse gaze opacity image compares to other visualizations of viewing, specifically a scan path, heat map, and a standard gaze opacity image. Midtrial stimuli creation is best achieved by creating multiple experiments in the eye-tracking software, allowing the stimuli for each subsequent experiment to be derived from the results of the preceding one.

Figure 5. Four images of gaze data. Top left image: scan path, displaying fixations as circles (size correlates to duration) and saccades as lines. Top right: heat map, displaying fixation duration from short (green) to long (red). Bottom left: gaze opacity image, showing areas viewed by the participant with unviewed areas blacked out. Bottom right: inverse gaze opacity image, showing areas not viewed by participant, with areas viewed blacked out (18). Stimulus images (labeled ‘start’ and ‘end’) adapted from animations created by Resa Kelly (personal communication) (8). (see color insert) Showing a participant where they looked offers an opportunity to study changes in viewing patterns due to visual feedback. Participants are able to reflect on the areas of the screen to which they did not attend and to identify any chemically relevant features they did not consider initially. This approach also holds the potential to create more purposeful viewers by making participants aware of their viewing strategy. Figure 6 shows an inverse gaze opacity image, along with visualizations of where the participant directed their visual attention after receiving this feedback on their initial viewing pattern. The heatmap and scanpath images show that the participant once again viewed the darkened areas 19 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

that they had previously viewed, but also looked at additional chemically relevant features during their second viewing.

Figure 6. The top image is an inverse opacity visualization of the participant’s viewing patterns from the first half of the study. This image was exported midtrial and inserted into the second half of the eye-tracking study. The bottom two images show where the participant looked when they were shown their initial viewing pattern. The heatmap (bottom left) and the scanpath (bottom right) together show that the participant viewed new chemically relevant features when shown their initial viewing pattern (18). The initial stimulus image is from a video developed by Resa Kelly (personal communication) based on a previously developed concept (8).

Analyzing Data Exporting Data Eye-tracking data records where and when someone looks at visual stimuli. The equipment specifications vary between machines, but additional metrics often provide insight into the reliability of the data as well as the viewing process. These additional metrics may include pupil diameter, mouse position or clicks, gaze vector, number of blinks/saccades/fixations/samples, individual eye deviations, and length of viewing (Figure 7). Most systems also provide a metric assessing the quality of the data collected, such as a confidence ranking (Tobii assigns each data point a validity code on a 0-4 scale, indicating confidence that 20 VandenPlas et al.; Eye Tracking for the Chemistry Education Researcher ACS Symposium Series; American Chemical Society: Washington, DC, 2018.

both eyes were located and tracked correctly) (16) or a tracking ratio (see Chapter 3 for more information). Often the analysis software provided with the equipment can process that information so the data are summarized with images (such as scanpaths, heatmaps, or an AOI sequence chart) and preliminary conclusions can be drawn. Eye trackers collect a large amount of data during each trial and careful consideration is needed to avoid turning a large complex quantitative data set into a qualitative snapshot that can be misleading. By not digging into the data behind those images or considering the research questions beyond those answerable by the analysis metrics, an opportunity may be lost. Exporting and quantitatively analyzing the eye-tracking data has the potential to reveal a more nuanced and possibly extremely valuable data set. One option is to export the data and analyze using R; Chapter 6 explores how R can be used to analyze eye-tracking data.

Figure 7. The export data screen from an SMI system (left) versus a Tobii system (right). Exporting eye-tracking data increases analysis options.

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Some systems have variable settings that allow researchers using eye trackers from different companies to integrate their data. For example, the REDn 60 Hz eye tracker allows a researcher to modify the sampling frequency from 60 Hz down to 30 Hz, providing the flexibility to share data with systems that have lower sampling frequencies. Being able to shift the settings to accommodate collaborative research offers additional flexibility. Knowing how to export your data and analyze externally using third-party software (such as R) can be extremely helpful if your software license expires, you want to investigate questions outside the scope of your analysis software limitations, or intersystem collaboration is of interest. It is your data and often there are questions to investigate and valuable analysis possible beyond the limitations of the software that came with the eye tracker.

Conclusion Practical considerations always need to be taken into account when planning and executing any research study, but particularly when eye tracking is involved. The researcher must be aware of the benefits eye tracking offers their study, including information about attention allocation and behavioral patterns, but researchers must also be aware of the limitations of their data. Eye tracking is not capable of informing the researcher about why participants look where they do, and it is frequently beneficial to collect data from additional sources such as drawings or participant interviews to help support the results of eye-tracking analyses. Based on these strengths and limitations, the researcher must carefully select an appropriate eye tracker and set up a research space that will allow for the collection of necessary data. Similar foresight is necessary when planning the participant’s task with a focus on the type of data that needs to be collected, as well as how this data might be analyzed. Consider running a pilot study to see if the data analysis addresses the research questions you hoped to investigate as you may need to shift the settings or reconsider the stimuli used (for more discussion of stimuli design, see Chapter 3). Hiccups in obtaining IRB approval, as well as collecting and analyzing data, can be minimized by considering these issues during the planning phase of an eye-tracking study.

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