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    Using Machine Learning Techniques to Predict Introductory Programming Performance


    Bergin, Susan and Mooney, Aidan and Ghent, John and Quille, Keith (2015) Using Machine Learning Techniques to Predict Introductory Programming Performance. International Journal of Computer Science and Software Engineering (IJCSSE), 4 (12). pp. 323-328. ISSN 2409 - 4285

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    Abstract

    Learning to program is difficult and can result in high drop out and failure rates. Numerous research studies have attempted to determine the factors that influence programming success and to develop suitable prediction models. The models built tend to be statistical, with linear regression the most common technique used. Over a three year period a multi-institutional, multivariate study was performed to determine factors that influence programming success. In this paper an investigation of six machine learning algorithms for predicting programming success, using the predetermined factors, is described. Naïve Bayes was found to have the highest prediction accuracy. However, no significant statistical differences were found between the accuracy of this algorithm and logistic regression, SMO (support vector machine), back propagation (artificial neural network) and C4.5 (decision tree). The paper concludes with a recent epilogue study that re-validates the factors and the performance of the naïve Bayes model.

    Item Type: Article
    Additional Information: This work is licensed under a Creative Commons Attribution 3.0 Unported License. https://creativecommons.org/licenses/by/3.0/
    Keywords: Learning to Program; Programming Predictors; Machine Learning; Naïve Bayes;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8682
    Depositing User: Dr. Susan Bergin
    Date Deposited: 25 Aug 2017 11:10
    Journal or Publication Title: International Journal of Computer Science and Software Engineering (IJCSSE)
    Publisher: International Journal of Computer Science and Software Engineering
    Refereed: Yes
    URI:

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