What Does Teaching Sound Like? Introducing DART – Decibel Analysis for Research in Teaching – A New Tool for Systematically Analyzing Teaching Practices across Instructors and Institutions
What proportion of STEM (science, technology, engineering, and math) instructors in higher education regularly employ teaching strategies beyond lecture? What is the probability that an undergraduate STEM student would have the opportunity to speak, write, or discuss their ideas about science with peers in every class session for every course they take? Here, we describe the development and application of the machine learning-derived algorithm Decibel Analysis for Research in Teaching (DART), which can analyze thousands of hours of STEM course audio recordings quickly, with minimal costs, and without need for human observers (PNAS, 2017). DART analyzes the volume and variance of classroom sound recordings to predict the quantity of time spent on Single Voice (e.g., lecture with question and answer), Multiple Voice (e.g., pair discussion), and No Voice (e.g., clicker question thinking) activities with ~90% accuracy. We envision DART as a tool for immediate feedback for individual instructors, as well as a system with which departments and institutions can regularly capture, assess, compare, and demonstrate their added educational value by showing the extent to which their instructors employ effective instructional practices for students.
Dr. Kimberly Tanner is a tenured Professor of Biology at San Francisco State University. She directs SEPAL – the Science Education Partnership and Assessment Laboratory, which is focused on understanding how people learn science, especially biology. Her research in biology education holds the promises of revealing insights into preconceptions and misconceptions in biology that can guide strategies for curriculum improvement and teaching reform. Trained as a research neurobiologist, Dr. Tanner has been nationally and internationally recognized for both her research and her teaching in biology, including receiving the 2012 National Outstanding Undergraduate Science Teacher Award from the Society for College Science Teachers and the 2017 Bruce Alberts Science Education Award from the American Society for Cell Biology.
Mike Wong is a Research Associate at the Center for Computing for Life Sciences (CCLS) at San Francisco State University. He has held this position for over a decade, assisting faculty in research and advising graduate students. He earned a BS in Biochemistry from UC Davis and an MS in Computer Science at SFSU under Dr. Dragutin Petković, who is the director of CCLS. Previously Mike was a Computer and Software Engineer at Agilent Technologies, formerly Hewlett-Packard, building Optical Spectrum Analyzers and CAD Library automation and management systems. Mike was a Pharmacy Technician for nearly a decade, working at a small family pharmacy in Potrero Hill, in his native city of San Francisco.