Using Learner Data to Influence Performance during Adaptive Tutoring Experiences

摘要

During computer-based tutoring sessions, Intelligent Tutoring Systems (ITSs) adapt planning and manage real-time instructional decisions. The link between learner data and enhanced performance is the adaptive tutoring learning effect chain through which learner data informs learner state classification which in turn informs optimal instructional decisions to enhance performance. This paper examines the roles and influence of learner data in both short-term (also called run-time or session) and long-term (also called persistent) learner models used to support adaptive tutoring decisions within the Generalized Intelligent Framework for Tutoring (GIFT). To enhance the usability of tutoring systems and learner performance, recommendations for the design of future learner models are also presented.