The Behavior of Tutoring Systems
International Journal of Artificial Intelligence in Education
Student Models that Invite the Learner In: The SMILI:() Open Learner Modelling Framework
International Journal of Artificial Intelligence in Education
Can Help Seeking Be Tutored? Searching for the Secret Sauce of Metacognitive Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors
International Journal of Artificial Intelligence in Education
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Self-Assessment in the REAP Tutor: Knowledge, Interest, Motivation, & Learning
International Journal of Artificial Intelligence in Education
Hi-index | 0.00 |
Helping students' improve their metacognitive and self-regulation skills holds the potential to improve students' ability to learn independently. Yet, to date, there are relatively few success stories of helping students enhance their metacognitive skills using interactive learning environments. In this paper we describe the Self-Assessment Tutor, an intelligent tutoring system for improving the accuracy of the judgments students make regarding their own knowledge. A classroom evaluation of the Self-Assessment Tutor with 84 students found that students improved their ability to identify their strengths while working with the Self-Assessment Tutor. In addition, students transferred the improved self-assessment skills to corresponding sections in the Geometry Cognitive Tutor. However, students often failed to identify their knowledge deficits a-priori and failed to update their assessments following unsuccessful solution attempts. This study contributes to theories of Self-Assessment and provides support for the viability of improving metacognitive skills using intelligent tutoring systems.