  Intelligent tutoring systems leverage AI models of expert learning and
student knowledge to deliver personalized tutoring to students. While these
intelligent tutors have demonstrated improved student learning outcomes, it is
still unclear how teachers might integrate them into curriculum and course
planning to support responsive pedagogy. In this paper, we conducted a design
study with five teachers who have deployed Apprentice Tutors, an intelligent
tutoring platform, in their classes. We characterized their challenges around
analyzing student interaction data from intelligent tutoring systems and built
VisTA (Visualizations for Tutor Analytics), a visual analytics system that
shows detailed provenance data across multiple coordinated views. We evaluated
VisTA with the same five teachers, and found that the visualizations helped
them better interpret intelligent tutor data, gain insights into student
problem-solving provenance, and decide on necessary follow-up actions - such as
providing students with further support or reviewing skills in the classroom.
Finally, we discuss potential extensions of VisTA into sequence query and
detection, as well as the potential for the visualizations to be useful for
encouraging self-directed learning in students.
