Abstract
We describe a new technique to represent, classify, and use programs written by novices as a base for automatic hint generation for programming tutors. The proposed linkage graph representation is used to record and reuse student work as a domain model, and we use an overlay comparison to compare in-progress work with complete solutions in a twist on the classic approach to hint generation. Hint annotation is a time consuming component of developing intelligent tutoring systems. Our approach uses educational data mining and machine learning techniques to automate the creation of a domain model and hints from student problem-solving data. We evaluate the approach with a sample of partial and complete, novice programs and show that our algorithms can be used to generate hints over 80 percent of the time. This promising rate shows that the approach has potential to be a source for automatically generated hints for novice programmers.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Barnes, T., Stamper, J.: Automatic hint generation for logic proof tutoring using historical data. Journal Educational Technology & Society, Special Issue on Intelligent Tutoring Systems 13(1), 3–12 (2010)
Barnes, T., Stamper, J.: Using Markov decision processes for student problem-solving visualization and automatic hint generation. In: Handbook on Educational Data Mining. CRC Press (2010)
Jin, W., Lehmann, L., Johnson, M., Eagle, M., Mostafavi, B., Barnes, T., Stamper, J.: Towards Automatic Hint Generation for a Data-Driven Novice Programming Tutor. In: Workshop on Knowledge Discovery in Educational Data, 17th ACM Conference on Knowledge Discovery and Data Mining (2011)
Stamper, J., Barnes, T., Croy, M.: Enhancing the automatic generation of hints with expert seeding. To appear in Intl. Journal of AI in Education, Special Issue “Best of ITS” (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, W., Barnes, T., Stamper, J., Eagle, M.J., Johnson, M.W., Lehmann, L. (2012). Program Representation for Automatic Hint Generation for a Data-Driven Novice Programming Tutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol 7315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-30950-2_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30949-6
Online ISBN: 978-3-642-30950-2
eBook Packages: Computer ScienceComputer Science (R0)