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International Conference on Artificial Intelligence in Education

AIED 2017: Artificial Intelligence in Education pp 490–493Cite as

Data-Driven Generation of Rubric Parameters from an Educational Programming Environment

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10331))

Abstract

We demonstrate that, by using a small set of hand-graded students, we can automatically generate rubric parameters with a high degree of validity, and that a predictive model incorporating these rubric parameters is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing more open-ended assignments. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem – a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric parameters using low-level data from the Alice programming environment.

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References

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Author information

Authors and Affiliations

  1. Human-Computer Interaction Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Nicholas Diana, Michael Eagle & John Stamper

  2. SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, USA

    Shuchi Grover, Marie Bienkowski & Satabdi Basu

Authors
  1. Nicholas Diana
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  2. Michael Eagle
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  3. John Stamper
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  4. Shuchi Grover
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  5. Marie Bienkowski
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  6. Satabdi Basu
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Corresponding author

Correspondence to Nicholas Diana .

Editor information

Editors and Affiliations

  1. Human-Centered Multimedia, University of Augsburg, Augsburg, Germany

    Elisabeth André

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Ryan Baker

  3. Department of Psychology, University of Memphis, Memphis, Tennessee, USA

    Xiangen Hu

  4. Ateneo de Manila University, Quezon City, Philippines

    Ma. Mercedes T. Rodrigo

  5. University of Sussex, Brighton, United Kingdom

    Benedict du Boulay

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© 2017 Springer International Publishing AG

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Cite this paper

Diana, N., Eagle, M., Stamper, J., Grover, S., Bienkowski, M., Basu, S. (2017). Data-Driven Generation of Rubric Parameters from an Educational Programming Environment. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_47

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  • DOI: https://doi.org/10.1007/978-3-319-61425-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61424-3

  • Online ISBN: 978-3-319-61425-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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