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Professional Informatics in Support of Inclusive and Equitable Teaching Practices

$300,000FY2022EDUNSF

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). College instructors often receive little to no feedback about their teaching or teaching strategies. Moreover, stark gender and racial disparities in STEM classrooms point to issues of inequality and uneven quality of learning experiences. This research project is designed to explore the use of sensing systems such as cameras and microphones in classrooms to collect data about how instructors teach. That data can then form the basis for personalized feedback on teaching practices. This overall program of investigation aims to reframe personal informatics, the use of self-tracked data to achieve one’s goals, as professional informatics (Pro-I) that can help STEM instructors improve their craft and, in so doing, create more equitable learning experiences. Such improvements hold promise especially, to increase STEM participation among students who are underrepresented in their participation in STEM fields of study. This research also has the potential to contribute to a general understanding of the use of sensing technologies in classrooms as a teaching aid and provide materials that aid self-reflection and improvement that could benefit many faculty. The principal investigator seeks to use Pro-I technologies to help STEM instructors further develop their professional practice with a view towards creating more equitable learning experiences in their undergraduate courses. Prevalent sources of feedback to faculty about their teaching, if received at all, are based on human observations, which do not always scale well. This project is designed to use classroom sensing systems to collect multimodal data such as movement patterns, gaze, facial expressions, and audio to provide a nuanced picture of teaching behaviors. The resulting data will be the basis for multimodal analytics designed to help instructors reflect on their teaching practices and derive actionable insights for improvement. This research could extend two existing technologies: EduSense a computer-vision based classroom sensing system and Edulyze an analytics pipeline for processing and visualizing multimodal classroom data. This project is comprised of three studies. First, educational stakeholders such as instructors, students, and administrators will be interviewed about their perceptions of privacy, agency, and adoption of Pro-I data and systems. Second, classroom data will be collected through EduSense and examined through Edulyze with particular attention to how analyses influence instructors’ self-efficacy and motivation to improve their teaching. Analyses will also examine the design of data visualizations for multimodal classroom data. Third, based on the results of the first two studies, a prototype Pro-I system will be developed and deployed for a semester to examine the effects of reflecting on the resulting analyses. This research has the potential for important contributions: increased understanding of the perceptions of privacy, agency, and adoption of Pro-I systems as they relate to the ethical design of classroom sensing systems; empirical evidence of how Pro-I data can impact instructors’ self-efficacy and motivation to improve their teaching; and the Pro-I technology that is intended to scaffold instructor reflections about classroom data as a means to further develop professional vision for equitable teaching and learning. The project responds to the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program that aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

View original record on NSF Award Search →