Eye-Tracking Technology and eLearning September 21, 2010Posted by elearningtyro in : Useful Resources , trackback
This guest post is contributed by Roger Elmore. He welcomes your comments at his email Id: email@example.com
Recent technological advancements have allowed eLearning developers to track learners’ eye movements as they complete various tasks within a specific lesson or course module. This technology can be extremely useful in two areas: 1) long-term development of courses and 2) short-term/real-time feedback for instructors. Essentially, this technology could greatly improve educators’ ability to improve how eLearning practices can best serve students.
The potential comes from a current trend in research to find new ways to evaluate students’ engagement with eLearning material. One specific project that seems to be breaking new ground in this area is AdELE (adaptive elearning with eye-tracking). Those behind AdELE say that such technology could improve both the teaching process and the learning process. Authors of another recent research paper, titled “Eye-tracking Users’ Behavior in Relation to Cognitive Style within an E-Learning Environment” agree, especially in regard to the way technology could help users personalize their eLearning experience, saying that there are “two implications in the design of e-learning and hypermedia applications: a) the psychometric tool and theory are both suitable for identifying types of users and b) the [Cognitive Styles Analysis] reveals differences in information processing and may be used as a personalization parameter.”
Results of general research concerning eye movement among those who learn via different cognitive styles can significantly help eLearning developers to create more efficient courses in the long term. By reevaluating and constantly updating courses, educators can ensure that their content and methods are benefitting students. After analyzing the eye positions and movement as well as score results of a particular test group, developers could filter from their content the less relevant material. Additionally, they could help create customizable applications within each module that allow different kinds of learners to select how the information is presented in order to best meet their particular learning style.
Furthermore, with this technology, instructors could monitor how their students process the information and work through the course. For example, the data concerning students’ eye movements could be stored in each student’s account, so that the instructor and student could make adjustments to the course as needed. If a student’s data suggests that he or she has recently been tired while studying, the student could schedule a different study period. If a recent set of scores are low, the instructor could check to see how the student looked at the course material and reassign it or present it in a new form in order to help the student. Of course, such an application would have to be used with care, otherwise the instructor could find him or herself wading through files of raw data rather than interacting with students.