A Design Study of the Artificial Intelligence-Augmented Motivation Indicator System

• In Event: AERA Poster Session 13
  In Poster Session: Looking at Online Learning From Various Perspectives, Methodologies, and Contexts (San Diego)

Abstract

In online learning, motivation encourages learners to initiate certain actions, use appropriate strategies to achieve their goals, and sustain actions even in difficult environments. Therefore, detecting students’ motivation accurately and on time is crucial as educators can easily identify what learning contents, activities, and instructional methods significantly undermine learners’ motivation in the online learning environment. To measure students’ real-time motivation level in online learning, we developed Artificial Intelligence-Augmented Motivation Indicator (AIMI) system. This study aims to validate the accuracy of AIMI by comparing motivation levels measured by both this tool and traditional paper-based survey. As a result, the motivation values generated by the AIMI system demonstrated a high level of accuracy, with an error rate of only about 10%.

Authors

• Nam Ju Kim, University of Miami

• Min Kyu Kim, Georgia State University