A General Multi-method Approach to Design-Loop Adaptivity in Intelligent Tutoring Systems

Design-loop adaptivity, which involves data-driven redesign of an instructional system based on student learning data, has shown promise in improving student learning. We present a general, systematic approach that combines new and existing data mining and instructional design methods to redesign intelligent tutors. Our approach is driven by the main goal of identifying knowledge components that are demonstrably difficult for students to learn and to optimize effective and efficient practice of them. We applied this approach to redesigning an algebraic symbolization tutor. Our classroom study with 76 high school freshmen shows that, compared to the original tutor, the redesigned tutor led to higher learning efficiency on more difficult skills, higher learning gain on unscaffolded whole tasks, and more robust transfer to less practiced tasks. Our work provides general guidance for performing design-loop adaptations for continuous improvement of intelligent tutors.