Abstract
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 data-driven 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 across complex, creative problem solutions to provide data-driven feedback for intelligent tutoring.
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Hicks, A., Peddycord III, B., Barnes, T.: Building games to learn from their players: generating hints in a serious game. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 312–317. Springer, Heidelberg (2014)
Moskal, B., Lurie, D., Cooper, S.: Evaluating the effectiveness of a new instructional approach. ACM SIGCSE Bulletin 36(1), 75–79 (2004)
Rivers, K., Koedinger, K.: Automatic generation of programming feedback: a data-driven approach. In: The First Workshop on AI-supported Education for Computer Science (AIEDCS 2013) (2013)
Rivers, K., Koedinger, K.R.: Automating hint generation with solution space path construction. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 329–339. Springer, Heidelberg (2014)
Stamper, J., Eagle, M., Barnes, T., Croy, M.: Experimental evaluation of automatic hint generation for a logic tutor. Artificial Intelligence in Education (AIED) 22(1), 3–17 (2013)
Utting, I., Cooper, S., Kölling, M.: Alice, Greenfoot, and Scratch-a discussion. ACM Transactions on Computing Education (TOCE) 10(4) (2010)
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Price, T.W., Barnes, T. (2015). Creating Data-Driven Feedback for Novices in Goal-Driven Programming Projects. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_132
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DOI: https://doi.org/10.1007/978-3-319-19773-9_132
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