My name is Thomas W. Price, and I am a Ph.D. candidate in Computer Science at North Carolina State University. I work in the Center for Educational Informatics under Dr. Tiffany Barnes in the Game2Learn lab.
My research goal is to reimagine educational programming environments as adaptive, data-driven systems that support students automatically as they pursue learning goals that are meaningful to them. I believe that every student should be able to learn computing with the support they need to be successful, working on projects that suit their values and interests. My research sits at the intersection of Computing Education Reserch (CER), Educational Data Mining (EDM) and Intelligent Tutoring Systems (ITS). In my research, I have:
You can learn more about me and my research by:
My Ph.D. disseration focuses on the design and evaluation of iSnap, a block-based programming environment that supports students automatically with data-driven hints and feedback. You can learn more about iSnap, the algorithms that power it, and the datasets I have collected at go.ncsu.edu/isnap.
You can see all of my publications here, but here are some recent highlights:
By Thomas W. Price and Tiffany Barnes. Presented at the Second Blocks and Beyond Workshop at the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 2017. Read the paper or see the presentation slides.
Abstract: Block-based programming environments make learning to program easier by allowing learners to focus on concepts rather than syntax. However, these environments offer little support when learners encounter difficulty with programming concepts themselves, especially in the absence of instructors. Textual programming environments increasingly use AI and data mining to provide intelligent, adaptive support for students, similar to human tutoring, which has been shown to improve performance and learning outcomes. In this position paper, we argue that block-based programming environments should also include these features. This paper gives an overview of promising research in intelligent support for programming and highlights the challenges and opportunities for applying this work to block-based programming.
By Thomas W. Price, Zhongxiu Liu, Veronica Cateté and Tiffany Barnes. Presented at the International Computing Education Research (ICER) Conference, 2017. Read the paper or see the presentation slides.
Abstract: When novice students encounter difficulty when learning to program, some can seek help from instructors or teaching assistants. This one-on-one tutoring is highly effective at fostering learning, but busy instructors and large class sizes can make expert help a scarce resource. Increasingly, programming environments attempt to imitate this human support by providing students with hints and feedback. In order to design effective, computer-based help, it is important to understand how and why students seek and avoid help when programming, and how this process differs when the help is provided by a human or a computer. We explore these questions through a qualitative analysis of 15 students' interviews, in which they reflect on solving two programming problems with human and computer help. We discuss implications for help design and present hypotheses on students' help-seeking behavior.