The research examines the sharing of Personally Identifiable Information (PII) by U.S. educational institutions on social media (X, formerly Twitter), with the goal of assessing potential privacy risks to students.
This project aims to understand how students develop their process of working on computer science projects, and testing a new approach to help CS students. This project is a collaboration between UTK and Pellissippi State Community College (PSCC), and is funded by the National Science Foundation.
This is a NSF CAREER project. It advances middle and high school students' data modeling in ecological contexts by taking a Bayesian approach that is supported and studied as an informal statistical inference framework.
This project is using an intensive longitudinal survey method - experience sampling - to explore how levels of anxiety change over time in introductory biology classes and how anxiety may impact final grade.
This paper presents an introductory and overview for the konfound() R package which is a sentiatively analysis package to quantify the robustness of causal inferences. This paper provides an overview of two core functions within the package: konfound() and pkonfound().
The NSF-funded project, Computer Science for Appalachia (CSA), led by Dr. Joshua Rosenberg and Dr. Amir Sadovnik, aims to address this by creating a CS vision and supporting teachers in planning and implementing responsive strategies.
This project is a three-year National Science Foundation-funded project focused on advancing methodologies that combine grounded theory based approaches with computational analyses of classroom video data. The interdisciplinary team is based at three institutions: the University of Illinois at Urbana-Champaign, Middle Tennessee University, and the University of Tennessee at Knoxville.