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Predicting Progress in a Large-Scale Online Programming Course

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Artificial Intelligence in Education (AIED 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13916))

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Abstract

With vast amounts of educational data being generated in schools, educators are increasingly embracing data mining techniques to track student progress, especially in programming courses, a growing area of computer science education research. However, there are few accurate and interpretable methods to track student progress in programming courses. To bridge this gap, we propose a decision tree approach to predict student progress in a large-scale online programming course. We demonstrate that this approach is highly interpretable and accurate, with an overall average accuracy of 88% and average dropout accuracy of 82%. Additionally, we identify important slides such as problem slide which significantly impact student outcomes.

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia

    Vincent Zhang, Bryn Jeffries & Irena Koprinska

  2. Grok Academy, PO Box 144, Broadway, Sydney, NSW, 2007, Australia

    Bryn Jeffries

Authors
  1. Vincent Zhang
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  2. Bryn Jeffries
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  3. Irena Koprinska
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Corresponding author

Correspondence to Vincent Zhang .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  4. Despacho 3.01, UNED-Grupo de Investigación aDeNu, Madrid, Spain

    Olga C. Santos

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhang, V., Jeffries, B., Koprinska, I. (2023). Predicting Progress in a Large-Scale Online Programming Course. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_76

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36271-2

  • Online ISBN: 978-3-031-36272-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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