Dr. Thomas Price
Thomas Price is an Assistant Professor of Computer Science at North Carolina State University, who directs the HINTS Lab. His primary research goal is to develop learning environments that automatically support students through AI and data-driven help features. His work has focused on the domain of computing education, where he has developed techniques for automatically generating programming hints and feedback for students in real-time by leveraging student data. He has evaluated the efficacy of innovative programming technologies, including block-based and frame-based programming environments, and has designed intelligent support features that integrate with these technologies.
Dr. Price completed his M.S. and Ph.D. degrees at NCSU in 2015 and 2018, respectively, where he was recognized as the College of Engineering Doctoral Scholar of the Year (2018). His research has earned Exemplary Paper Awards at the International Conference on Educational Data Mining (2016) and the ACM Technical Symposium on Computer Science Education (2017). Dr. Price’s research and teaching are driven by a desire to make computing education available to a larger and more diverse population learners, and Dr. Price has been recognized by the STARS Computing Corps for his leadership in computing outreach. His current research projects include exploring new, data-driven methods for supporting students, evaluating these systems in classroom and online learning settings, and investigating how students seek and use help when learning.
Ph.D. Student [ Google Scholar ]
Samiha is passionate about helping students learn how to code and improving students’ problem solving skills. Her research focus on exploring different design choices of automated feedback to improve students’ performance, learning, and affective outcomes like persistence and self-efficacy. Samiha’s current research includes adding additional types of support in iSnap block-based programming language, and PCRS (online practice environment for python programming language) using both expert-authored and data-driven feedback to improve students’ outcomes.
Wengran (Emma) Wang
I want to understand the cognitive, psychological, and social journeys a students takes to become a programmer, and build tools to support such development. I believe programming support should be natural, simple, and should be helping all kinds of learners. As I’m currently in my second year, I am focusing on learning and exploring methods and ideas in code analysis and student programming experience that create research space for promoting novices’ programming self-efficacy and creativity.
Before joining HINTS Lab, I did my undergraduate study in Zhejiang University, Hangzhou, China.
Yang Shi is a PhD student of Computer Science at North Carolina State University. He completed his master degree in Computer Science at University of Georgia. Yang is interested in developing data-driven methods to improve student and programmer code representations to enhance the intelligent learning environment and student models. He is currently working on projects about extracting features of programmer code snippets and the applications of the extracted features.
James is interested on how we can use data-driven methods to make learning Computer Science more accessible and authentic. During his undergraduate degree at the University of Delaware, he co-designed the course “Computational Thinking in Music”, in which non-STEM majors used statistical methods and CS skills to compose a song in the style of their favorite artist. James’ current research is investigating how students learn and conceptualize machine learning models. In addition to this, he is also interested in understanding how novice programmers seek for help on the Internet when they are stuck.
Yudong worked with Rui on developing new features for iSnap, including data-driven example feedbacks. He is currently working with Emma to collect data and information on students’ experience with data-driven example feedbacks.
Dr. Rui Zhi
Ph.D. Alumnus[ Google Scholar ]
Rui is interested in motivating students in learning programming concepts by applying cognitive science theories. Rui’s research investigates the impact of programming worked examples on students performance, programming efficiency and learning. He has conducted studies on their efficacy in the BOTS education game and in the iSnap novice programming environment. He is currently working to develop novel data-driven methods to generate worked example steps automatically, which are customized to the student’s current code. Rui is co-advised by Dr. Tiffany Barnes in the Game2Learn lab.