Creating Data-Driven Feedback for Novices in Goal-Driven Programming Projects

Programming environments that afford the creation of media-rich, goal-driven projects, such as games, stories and simulations, are effective at engaging novice users. However, the open-ended nature of these projects makes it difficult to generate ITS-style guidance for students in need of help. In domains where students produce similar, overlapping solutions, data-driven techniques can leverage the work of previous students to provide feedback. However, our data suggest that solutions to these projects have insufficient overlap to apply current datadriven methods. We propose a novel subtree-based state matching technique that will find partially overlapping solutions to generate feedback across diverse student programs. We will build a system to generate this feedback, test the technique on historical data, and evaluate the generated feedback in a study of goal-driven programming projects. If successful, this approach will provide insight into how to leverage structural similarities...

• Type: Journal
• Year: 2015
• Where: AIED
• Authors: Thomas W. Price, Tiffany Barnes
• Keywords: AIED 2015, Artificial Intelligence