Creating intelligent learning technologies from data has unique potential to transform the American educational system, by building a low cost way to adapt learning environments to individual students, while informing research on human learning. Our research constructs adaptive support for teaching and learning in logic, discrete mathematics, and other STEM domains using a data-driven approach. From the extensive but tractable student performance data in computer-aided learning environments, we automatically construct models using Markov Decision Processes and dimensionality reduction methods to leverage past data to assess student performance, direct a student's learning path, and provide contextualized hints. We use machine learning techniques to expand our problem-specific models to bootstrap the construction of new tutors and learn about student learning. For teachers and learning researchers, we have created InVis, a visualization and analysis tool to graphically and interactively model student solutions annotated with performance data that reflects frequency, tendency to commit future errors, and closeness to a final solution. In this talk I will present this research, and highlights from my other work including: my new course on the Beauty and Joy of Computing, the STARS Alliance for broadening participation, creating games for education, exercise, and energy, and international computing outreach.

Tiffany Barnes (UNCC) is Associate Professor of Computer Science at the University of North Carolina at Charlotte. She received the B.S. and M.S. degrees in Computer Science and Mathematics, and the Ph.D. degree in Computer Science from N.C. State. A member of Phi Beta Kappa and the NC State Golden Chain Society, she has served as Chair (2008) and Program Chair (2009) of the Educational Data Mining conference, Chair of the STARS Celebration conference (2011), Program Track Chair for the Foundations of Digital Games conference (2009), the ACM Special Interest Group on Computer Science Education Board (2010-2013), the Board of Directors for the International Educational Data Mining Society (2011-2013), Associate Editor for the Journal of Educational Data Mining (2008-2010), and Guest Editor for the IEEE Computer Graphics and Applications Special Issue on Serious Games (2009). Dr. Barnes received an NSF CAREER Award for her novel work in using data and educational data mining to add intelligence to STEM learning environments. Dr. Barnes is co-PI for the STARS Alliance, a consortium of universities that engage college students in outreach, research, and service to broaden participation in computing. Her research focuses on educational data mining, serious games for education, health, and energy, and broadening participation in computing education and research.