Utilizing the Generalized Intelligent Framework for Tutoring to Encourage Self-Regulated Learning

Abstract

Self-regulated learning is of particular importance to computer-based and online instruction, as students need to manage their own time and their interactions with the system. Elements of self-regulatory learning traditionally include the metacognitive strategies of the students (e.g., their knowledge of their planning, and assessment of their own progress), their management of educational goals (e.g., what information is most important to them, and should receive their primary attention), and the strategies that students use in order to study and retain the provided information [1, 2]. By incorporating feedback and guidance within computer-based learning activities it can encourage students to engage in successful self-regulated learning with a better awareness of their own cognition, and strategies. Intelligent tutoring systems can utilize adaptive scaffolding and guidance in order to support self-regulated student learning [3]. The Generalized Intelligent Framework for Tutoring (GIFT) [4] is an open-source adaptive tutoring system framework. The included tools within GIFT can be used to structure courses which guide individuals through the learning environment and are consistent with self-regulated best practices. The current paper includes a brief review of research into self-regulated learning in the context of computer-based and adaptive instruction. Further, the authoring capabilities of GIFT are discussed, and recommendations are given for future feature additions to GIFT which will benefit instructors who wish to develop courses that facilitate self-regulated learning.