Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming

Paaßen B, Jensen J, Hammer B (2016)
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds);Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.

Conference Paper | Published | English

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Barnes, Tiffany ; Chi, Min ; Feng, Mingyu
Abstract
The first intelligent tutoring systems for computer programming have been proposed more than 30 years ago, mostly focusing on well defined programming tasks e.g. in the context of logic programming. Recent systems also teach complex programs, where explicit modelling of every possible program and mistake is no longer possible. Such systems are based on data-driven approaches, which focus on the syntax of a program or consider the output for example cases. However, the system's understanding of student programs could be enriched by a deeper focus on the actual execution of a program. This requires a suitable data representation which encodes information of programming style as well as its functionality in a suitable way, thus offering entry points for automated feedback generation. In this contribution we propose a representation of computer programs via execution traces for example input and demonstrate the power of this representation in three key challenges for intelligent tutoring systems: identifying the underlying solution strategy, identifying erroneous solutions and locating the errors in erroneous programs for feedback display.
Publishing Year
Conference
9th International Conference on Educational Data Mining
Location
Raleigh, North Carolina, USA
Conference Date
2016-06-29 – 2016-07-02
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Paaßen B, Jensen J, Hammer B. Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In: Barnes T, Chi M, Feng M, eds. Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina, USA: International Educational Datamining Society; 2016: 183-190.
Paaßen, B., Jensen, J., & Hammer, B. (2016). Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In T. Barnes, M. Chi, & M. Feng (Eds.), Proceedings of the 9th International Conference on Educational Data Mining (pp. 183-190). Raleigh, North Carolina, USA: International Educational Datamining Society.
Paaßen, B., Jensen, J., and Hammer, B. (2016). “Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming” in Proceedings of the 9th International Conference on Educational Data Mining, Barnes, T., Chi, M., and Feng, M. eds. (Raleigh, North Carolina, USA: International Educational Datamining Society), 183-190.
Paaßen, B., Jensen, J., & Hammer, B., 2016. Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In T. Barnes, M. Chi, & M. Feng, eds. Proceedings of the 9th International Conference on Educational Data Mining. Raleigh, North Carolina, USA: International Educational Datamining Society, pp. 183-190.
B. Paaßen, J. Jensen, and B. Hammer, “Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming”, Proceedings of the 9th International Conference on Educational Data Mining, T. Barnes, M. Chi, and M. Feng, eds., Raleigh, North Carolina, USA: International Educational Datamining Society, 2016, pp.183-190.
Paaßen, B., Jensen, J., Hammer, B.: Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming. In: Barnes, T., Chi, M., and Feng, M. (eds.) Proceedings of the 9th International Conference on Educational Data Mining. p. 183-190. International Educational Datamining Society, Raleigh, North Carolina, USA (2016).
Paaßen, Benjamin, Jensen, Joris, and Hammer, Barbara. “Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming”. Proceedings of the 9th International Conference on Educational Data Mining. Ed. Tiffany Barnes, Min Chi, and Mingyu Feng. Raleigh, North Carolina, USA: International Educational Datamining Society, 2016. 183-190.
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