• Login
    View Item 
    •   Research Bank Home
    • Study Areas
    • Education
    • Education Conference Papers
    • View Item
    •   Research Bank Home
    • Study Areas
    • Education
    • Education Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Predicting the academic performance of international students on an ongoing basis

    Han, Binglan; Watts, Michael J.

    Thumbnail
    Share
    View fulltext online
    2016CITRENZ_1_Han_IntAcademicPerf_17-2.pdf (253.4Kb)
    Date
    2016-07
    Citation:
    Han, B., & Watts, M. J. (2016, July). Predicting the Academic Performance of International Students on an Ongoing Basis. Michael Verhaart, Emre Erturk, Arron Steele and Scott Morton (Ed.), ITx 2016 CITRENZ New Zealand's Conference of IT (pp.48-53)
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/3578
    Abstract
    The academic success of international students is crucial for many tertiary institutions. Early predictions of students’ learning outcomes allow for targeted support and therefore improved success rates. In this study, international students’ demographic information, past academic histories, weekly class attendance records, and assessment results in an ongoing course were used to develop models to predict student success and failure in the course on a weekly basis. The prediction models were produced with three decision tree classification algorithms: REPTree, J48 tree, and LMT on the data-mining platform WEKA. Of these, the LMT algorithm has the highest level of accuracy, but the REPTree and J48 models are simpler and easier to interpret. While the accuracies of all three models are above 75%, further research is needed to more accurately predict student failure at early stages.
    Keywords:
    Auckland Institute of Studies (AIS) courses, international students, academic performance, educational data mining, decision trees, assessment, REPTree, J48 tree, Logistic Model Tree (LMT)
    ANZSRC Field of Research:
    130303 Education Assessment and Evaluation
    Copyright Holder:
    Authors
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
    Metadata
    Show detailed record
    This item appears in
    • Education Conference Papers [254]

    Library home
    Send Feedback
    Research publications
    Unitec
    Moodle
    © Unitec Institute of Technology, Private Bag 92025, Victoria Street West, Auckland 1142
     

     

    Usage

    Downloads, last 12 months
    12
     
     

    Usage Statistics

    For this itemFor the Research Bank

    Share

    About

    About Research BankResearch at UnitecContact us

    Help for authors  

    How to add researchOpen Access GuideVersions Toolkit

    Register for updates  

    LoginRegister

    Browse Research Bank  

    EverywhereAcademic study areasAuthorDateSubjectTitleType of researchSupervisorThis CollectionAuthorDateSubjectTitleType of researchSupervisor

    Library home
    Send Feedback
    Research publications
    Unitec
    Moodle
    © Unitec Institute of Technology, Private Bag 92025, Victoria Street West, Auckland 1142