A Dashboard to Support Teachers During Students’ Self-paced AI-Supported Problem-Solving Practice

Abstract

Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced learning. To address this challenge, we conducted design-based research with 20 middle school teachers to create a novel real-time dashboard, Tutti, that helps a teacher monitor a class and decide which individual students to help, based on analytics from students’ tutoring software. Tutti is fully implemented and has been honed through prototyping and log replay sessions. A partial implementation was piloted in remote classrooms. Key design features are a two-screen design with (1) a class overview screen showing the status of each student as well as notifications of recent events, and (2) a deep dive screen to explore an individual student's work in detail, with both dynamic replay and an interactive annotated solution view. The project yields new insight into effective designs for a real-time analytics-based tool that may guide the design of other tools for K-12 teachers to support students in self-paced learning activities.