PIs: Tiffany Barnes, Min Chi
North Carolina State University
The researchers in this project will study fully data-driven systems to provide both scalable and individualized support for learners. Open-ended, media-rich visual programming environments such as Scratch and Snap represent the next-generation genre for engaging and inspiring students to learn programming. However, solving open-ended programming problems is a particularly challenging area for data-driven support due to the extremely large potential solution spaces and the very creative embellishments that make them attractive. This project integrates intelligent student support that is derived from data into the media-rich, open-ended problem solving in the Snap programming environment.
More specifically, the researchers will build and evaluate data-driven methods to provide hints, worked examples, and proactive positive feedback while students work on media-rich, open-ended programs that can interest and motivate diverse students. The researchers will conduct 6 studies in a cycle of iterative refinement to integrate data-driven hints, examples, and feedback for open-ended problems into the Snap programming environment. This enables the study of the combined impact of a media-rich environment with individualized support, and the structures that are created for adaptive support will provide insight into how novices learn to program. Snap is representative of novice programming environments, and is almost identical to Scratch, a popular programming language. More importantly, the techniques used are based on generic properties of computer programs and could be applied to many programming environments and problems, and the research questions addressed have general applicability across problem solving domains.