Beginning three NSF-funded projects
Joshua Rosenberg will be beginning three NSF-funded projects this fall. They are described in this post for the University of Tennessee, Knoxville, Department of Theory and Practice in Teacher Education newsletter. Here are the descriptions of the projects from the newsletter:
Understanding the Development of Interest in Computer Science: An Experience Sampling Approach is a two-year project that builds on Rosenberg’s dissertation research about how students develop an interest in computer science. Rosenberg’s dissertation research utilized an experience sampling method in which study participants were surveyed with short questions delivered via cell phones at regular intervals throughout the course of their learning during out-of-school summer STEM programs. This project uses a similar methodological approach in an undergraduate context. The $350,000 grant will support a postdoctoral scholar and a graduate research assistant.
CS for Appalachia: A Research-Practice Partnership for Integrating Computer Science into East Tennessee Schools is a two-year project led by Lynn Hodge, STEM education professor. The project is focused on developing a research-practice partnership among researchers, teachers and administrators to address challenges related to teaching computer science at the elementary level. Building upon the shared vision developed within the research-practice partnership, the $250,000 grant includes opportunities for K-5 teachers to engage in professional development about the Tennessee computer science standards.
Advancing Computational Grounded Theory for Audiovisual Data from STEM Classrooms is a three-year collaborative project among faculty at the University of Tennessee, Knoxville; Middle Tennessee State University (MTSU); and the University of Illinois, Urbana-Champaign (UIUC). The project is led by Christina Krist, UIUC assistant professor of curriculum and instruction. This project is focused on developing a new research methodology that uniquely combines two approaches: interpretative human-driven coding and newer machine learning techniques, also known as artificial intelligence. The aim is to use audio-visual data from STEM classrooms to study the kinds of teaching and learning of most interest to educational researchers rather than simply what can be studied using machine learning.The $1.3 million dollar grant will support a postdoctoral scholar at MTSU and graduate research assistants at UIUC.