EAGER: Leveraging Behavioral and Physiological Feedback in the Design of Affect-Sensitive Distance Learning


PIs: Lauren Wilcox, Elizabeth DiSalvo, David Joyner, Thomas Ploetz
Georgia Tech Research Corporation
Award Details

As the world workforce increasingly pursues technology-focused careers, we see more technology learning environments emerge, such as online courses. There are many advantages to online learning that make it appealing to adult learners, such as low cost, flexibility in times and pacing, and convenience of location. However, there are also many disadvantages that can be attributed to the lack of face-to-face interaction. In online learning environments, instructors cannot observe if students are motivated, engaged, lost or frustrated. This project will investigate the feasibility of using wearable technologies and other types of sensing to gather more context about the online learners. The research will develop techniques for incorporating these complementary sensing technologies to learn more about the online student’s environment and their subjective experiences during their participation in online courses. This will generate methods to measure social context in online learning environments. The second key aim of the project is to use insights from wearable devices and other sensing technologies, to design new online learning environments – and the live instruction being given to online students – to better meet learners’ needs.

The innovation in this project lies in multi-modal sensing, coupled with modeling of students’ cognitive and affective states. Our primary objectives are to collect and identify correlations between wearable sensor data and learning measures; to explore real-time interaction between modeling and online teaching; and to inform improvement of learning systems. The research team will study the needs of students, teachers and administrators of online courses in computer science. This will create a large database; computer infrastructure; methods for integrating across sensing, analytic, and modeling modalities. The project will develop a real-world pilot, with online learners and instructors. Studies of the pilot will confirm the feasibility and acceptability of the infrastructure, and the ability to capture signs of boredom, frustration, delight, and cognitive load.

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