Kenneth Holstein

Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms.

When used in K-12 classrooms, intelligent tutoring systems (ITSs) can be highly effective in helping students learn. However, they might be even more effective if designed to work together with human teachers, to amplify their abilities and leverage their complementary strengths. In the present work, we designed a wearable, real-time teacher awareness tool: mixed-reality smart glasses that tune teachers in to the rich analytics generated by ITSs, alerting them to situations the ITS may be ill-suited to handle. A 3-condition experiment with 286 middle school students, across 18 classrooms and 8 teachers, found that presenting teachers with real-time analytics about student learning, metacognition, and behavior had a positive impact on student learning, compared with both business-as-usual and classroom monitoring support without advanced analytics. Our findings suggest that real-time teacher analytics can help to narrow the gap in learning outcomes across students of varying prior ability. This is the first experimental study showing that real-time teacher analytics can enhance student learning. This research illustrates the promise of AIED systems that integrate human and machine intelligence to support student learning.

Opening Up an Intelligent Tutoring System Development Environment for Extensible Student Modeling

ITS authoring tools make creating intelligent tutoring systems more cost effective, but few authoring tools make it easy to flexibly incorporate an open-ended range of student modeling methods and learning analytics tools. To support a cumulative science of student modeling and enhance the impact of real-world tutoring systems, it is critical to extend ITS authoring tools so they easily accommodate novel student modeling methods. We report on extensions to the CTAT/Tutorshop architecture to support a plug-in approach to extensible student modeling, which gives an author full control over the content of the student model. The extensions enhance the range of adaptive tutoring behaviors that can be authored and support building external, student- or teacher-facing real-time analytics tools. The contributions of this work are: (1) an open architecture to support the plugging in, sharing, re-mixing, and use of advanced student modeling techniques, ITSs, and dashboards; and (2) case studies illustrating diverse ways authors have used the architecture.