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EAGER: Developing Teaching Assistant Expertise with a Sensor-Based Learning System: 1747997

Principal Investigator: Amy Ogan
CoPrincipal Investigator(s): John Zimmerman
Organization: Carnegie-Mellon University

While a college degree is essential for many jobs or career paths, many college students receive a less than ideal educational experience. For years, research has shown that moving away from large lectures and increasing student engagement and participation significantly improves learning. However, most colleges still rely on lectures where students passively receive information from professors, and instructors continue to teach in the ways in which they themselves were taught. This project addresses this large-scale societal problem of understanding and supporting instructor learning by investigating how to support teaching assistants (TAs) to acquire student-centered teaching skills before they transition to faculty. This exploratory research project will investigate the development of an advanced learning technology system that integrates multimodal sensory data to deliver near real-time data on teaching practices. The combined reflection and training system will provide rapid and frequent feedback and instruction on good strategies to support student-centered teaching beliefs and behaviors. In turn, the project will advance understanding of how instructors engage with personal informatics to impact beliefs and behaviors related to teacher professional development.

In this exploratory project, a learning system for TAs will be developed, consisting of an interconnected set of modules that support them in acquiring student-centered teaching practices through the provision of a dashboard with near-real-time multimodal classroom data. The system draws upon technical and socio-technical advances in sensing arrays, computer vision, intelligent environments, and personal informatics, together with frameworks of professional development in higher education. The project advances computing by expanding the capabilities of state of the art multimodal sensing approaches to achieve non-invasive sensing at classroom-scale. Through a series of design-based research studies with TAs who are teaching STEM college courses, the project investigates: a) the ways in which this system can present personal data to foster productive self-doubt, b) how engagement with personal data leads to a motivation to change, and c) how data-driven instruction can foster self-efficacy in teaching.

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