Personalization of Automated Guidance in a Web-Based Inquiry Science Environment

• In Event: Understanding and Using Learner Data in Technology Environments

Abstract

While automated guidance in online units has been found to successfully support learning, students can sometimes feel alienated rather than motivated because computerized guidance seems impersonal. In this study, we explore ways to improve learning and motivation by personalizing adaptive guidance. Standard adaptive guidance is compared to personalized adaptive guidance, which a) explains how guidance adapts to student work, b) explicitly indicates student progress, and c) refers to students by name. The personalized condition was more effective than the standard condition for performance on an embedded item in the unit, and led to higher pre to posttest gains among low prior knowledge students. These results suggest that increasing personalization of adaptive guidance can increase motivation and student learning.

Authors

• Charissa Tansomboon, University of California Berkeley

• Libby F. Gerard, University of California - Berkeley

• Jonathan Michael Vitale, University of California - Berkeley

• Marcia Linn, University of California - Berkeley