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Using Bayesian Networks and Machine Learning to Predict Computer Science Success

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Book cover ICT Education (SACLA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 963))

Abstract

Bayesian Networks and Machine Learning techniques were evaluated and compared for predicting academic performance of Computer Science students at the University of Cape Town. Bayesian Networks performed similarly to other classification models. The causal links inherent in Bayesian Networks allow for understanding of the contributing factors for academic success in this field. The most effective indicators of success in first-year ‘core’ courses in Computer Science included the student’s scores for Mathematics and Physics as well as their aptitude for learning and their work ethos. It was found that unsuccessful students could be identified with \(\approx \)91% accuracy. This could help to increase throughput as well as student wellbeing at university.

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Notes

  1. 1.

    http://www.educationaldatamining.org.

  2. 2.

    In South Africa, ‘Matric’ is name of the formal qualification level of pupils who have passed their secondary school (high school) education after school-year 12 before university—somewhat similar to the Austrian ‘Matura’ or the German ‘Abitur’.

  3. 3.

    http://www.norsys.com/download.html.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cape Town, Cape Town, South Africa

    Zachary Nudelman, Deshendran Moodley & Sonia Berman

Authors
  1. Zachary Nudelman
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  2. Deshendran Moodley
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  3. Sonia Berman
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Corresponding author

Correspondence to Zachary Nudelman .

Editor information

Editors and Affiliations

  1. Department of Information Systems, University of Cape Town, Cape Town, South Africa

    Salah Kabanda

  2. Computer Science, University of Cape Town, Rondebosch, Cape Town, South Africa

    Hussein Suleman

  3. University of Pretoria, Pretoria, South Africa

    Stefan Gruner

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Nudelman, Z., Moodley, D., Berman, S. (2019). Using Bayesian Networks and Machine Learning to Predict Computer Science Success. In: Kabanda, S., Suleman, H., Gruner, S. (eds) ICT Education. SACLA 2018. Communications in Computer and Information Science, vol 963. Springer, Cham. https://doi.org/10.1007/978-3-030-05813-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-05813-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05812-8

  • Online ISBN: 978-3-030-05813-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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