Visualizing Intelligent Tutor Interactions for Responsive Pedagogy

TL;DR

This paper presents VisTA, a visual analytics tool designed to help teachers interpret student interaction data from intelligent tutoring systems, thereby supporting more responsive and effective pedagogy.

Contribution

The paper introduces VisTA, a novel visual analytics system that aids teachers in analyzing intelligent tutor data to enhance responsive teaching strategies.

Findings

VisTA improved teachers' understanding of student problem-solving processes.

Teachers used VisTA to identify students needing additional support.

The system facilitated better decision-making for follow-up actions.

Abstract

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.