Forecasting the Future

inSight: An integrated mobile device eye-tracking software

My vision for the future is to take advantage of increasingly complex camera systems and computing processors that are already present in mobile devices in order to create an integrated eye-tracking software that can be used for a variety of intentions. This program, affectionately dubbed inSight, would allow for the mobile device user, or other permitted individuals, to access the measurements of the gaze data in order to extrapolate other understandings of the user.

Purpose

The purpose of the InSight program would be to analyze the gaze of individuals in order to provide an understanding of that particular person. While there may be a variety of implications, my intention would be to help educators:

  • Examine gaze to understand comprehension of the lesson content that is being delivered

  • Reflect on the effectiveness of a variety of teaching strategies or materials by measuring visual interest

  • Pre-emptively screen for vision-based conditions, such as dyslexia or dyscalculia, by analyzing vision patterns

  • Ensure literacy and numeracy skills are developing properly by understanding how the individual learner approaches new learning

  • Monitor eye movement and pupil dilation to help provide biometric data, correlating to user emotion and identifying states in which they are most receptive to new learning

  • Help inform the user experience (UX) design by identifying which features are most examined/visually appealing and those that may be distracting or take away from the learning to offer a more refined experience

Features

  • Unintrusive, embedded within mobile devices, and taking advantage of native hardware (camera and processors) to complete the eye-tracking. Would not be active unless the user chose to opt into the program

  • Built-in algorithm to analyze eye movements and alert the user/educator as to any concerns, such as improper decoding of words while reading. The algorithm would also be able to analyze for a specific intention, such as an educator trying to understand if the embedded video in their lesson was interesting or disengaging

  • An encrypted, cloud-based set of reference calibration recordings to help ensure the accuracy and efficiency of the system, utilizing the same idea as the GazeCapture program

  • An internal reference library of eye-tracking data that demonstrates typical eye movements and executive functioning to serve as comparison of atypical eve movement or behaviour

  • The InSight program would utilize a cloud-based storage function in order to navigate the limitation of video storage, as well as make the sharing of the recorded data simple

Example of inSight Usage

An educator assigns a reading task with accompanying comprehension questions to be completed by their students. The passage is sent out digitally to the class and each individual student can pull up the passage on their mobile device (personal or school owned).

As the students read the passage for comprehension and understanding, inSight is passively running on their mobile devices and feeding that data to the user interface, where both the student and teacher can log in to access the information. It creates a map of the students' eye movements and turns the tracking movements into numerical data, such as the time required to read the passage, the number of words that were re-read an excessive number of times, or inattentive periods compared to the internal reference library. The educator is then able to extrapolate a variety of information from the results.


  1. The teacher notices that Student A struggled with their comprehension question answers and spent time focused on the picture attached to the reading passage rather than the words. The teacher then distributes more visual pieces to Student A to match their learning preferences for future learning opportunities

  2. The system flags Student B's reading patterns, noting they were erratic, and matches recorded instances of a reading disability. The teacher meets one on one with the student, referring them for further reading support and testing.

  3. inSight flags Student C as having an eye gaze indicative of a stuttering and inconsistent pace, suggesting that the student may be struggling with the decoding of the language. The teacher meets later with that student to review reading strategies and schedules for further development of reading skills.

  4. inSight flags several students as having spent more than the necessary period of time fixated on the menu bar of the reading application, prompting the teacher to find a different, less distracting option for the next lesson. They also make adaptations to the UX of their personal website.