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Data Mining of Syntax Errors in a Large-Scale Online Python Course

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13356))

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Abstract

This paper investigates the common syntax errors students encounter when programming in Python using data from a large-scale online beginner course. We firstly analyse the error distribution to find differences between passing and failing students and then use clustering and Markov chains to identify clusters of student submissions with similar error pattern and how students move between these clusters during the course and between consecutive tasks. This type of analysis can be used by educators to understand student behaviour related to syntax errors and provide effective teaching support.

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References

  1. Denny, P., Luxton-Reilly, A., Tempero, E., Hendrickx, J.: Understanding the syntax barrier for novices. In: 16th Conference on Innovation and Technology in Computer Science Education, pp. 208–212 (2011)

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  2. Brown, N., Altadmri, A.: Investigating novice programming mistakes: educator beliefs vs student data. In: 10th Conference on International Computing Education, pp. 45–50 (2014)

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  3. NCSS Challenge. https://grokacademy.org/challenge

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Sydney, Sydney, Australia

    Jung A. Lee & Irena Koprinska

  2. Grok Academy and School of Computer Science, The University of Sydney, Sydney, Australia

    Bryn Jeffries

Authors
  1. Jung A. Lee
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  2. Irena Koprinska
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  3. Bryn Jeffries
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Corresponding author

Correspondence to Irena Koprinska .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Lee, J.A., Koprinska, I., Jeffries, B. (2022). Data Mining of Syntax Errors in a Large-Scale Online Python Course. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_124

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_124

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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