Data-Driven Feedback Beyond Next-Step Hints

Intelligent tutors have been shown to be as e↵ective as human tutors in supporting learning in many domains. Although they can be very e↵ective, the construction of intelligent tutors can be costly. One way to address this problem is to use previously collected data to generate domain models to provide intelligent feedback to otherwise non-personalized tutors. These data-driven methods for providing next-step hints have been successful in providing feedback to students in procedural problem solving tutors. We seek to expand on next-step hints with other data-driven methods. We outline three di↵erent interventions, all of which can be generated using previously collected student data.