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    Predicting introductory programming performance: A multi-institutional multivariate study


    Bergin, Susan and Reilly, Ronan (2006) Predicting introductory programming performance: A multi-institutional multivariate study. Computer Science Education, 16 (4). pp. 303-323. ISSN 0899-3408

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    Abstract

    A model for predicting student performance on introductory programming modules is presented. The model uses attributes identified in a study carried out at four third-level institutions in the Republic of Ireland. Four instruments were used to collect the data and over 25 attributes were examined. A data reduction technique was applied and a logistic regression model using 10-fold stratified cross validation was developed. The model used three attributes: Leaving Certificate Mathematics result (final mathematics examination at second level), number of hours playing computer games while taking the module and programming self-esteem. Prediction success was significant with 80% of students correctly classified. The model also works well on a per-institution level. A discussion on the implications of the model is provided and future work is outlined.

    Item Type: Article
    Keywords: introductory programming performance; multi-institutional; multivariate study; student performance; prediction;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8201
    Identification Number: https://doi.org/10.1080/08993400600997096
    Depositing User: Prof. Ronan Reilly
    Date Deposited: 09 May 2017 14:53
    Journal or Publication Title: Computer Science Education
    Publisher: Taylor & Francis
    Refereed: Yes
    URI:

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