• Admin Login
    View Item 
    •   Open Polytechnic Repository Home
    • Open Polytechnic Research
    • Information Systems and Technology
    • View Item
    •   Open Polytechnic Repository Home
    • Open Polytechnic Research
    • Information Systems and Technology
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Early prediction of student success: Mining students' enrolment data.

    Thumbnail
    View/Open
    Conference paper (379.0Kb)
    Metadata
    Show full item record
    Author
    Kovacic, Z.
    Keyword
    Study outcomes
    Distance learning
    Date
    2010
    Type
    Conference Paper
    Language
    en
    Abstract
    This paper explores the socio-demographic variables (age, gender, ethnicity, education, work status, and disability) and study environment (course programme and course block), that may influence persistence or dropout of students at the Open Polytechnic of New Zealand. We examine to what extent these factors, i.e. enrolment data help us in pre-identifying successful and unsuccessful students. The data stored in the Open Polytechnic student management system from 2006 to 2009, covering over 450 students who enrolled to 71150 Information Systems course was used to perform a quantitative analysis of study outcome. Based on data mining techniques (such as feature selection and classification trees), the most important factors for student success and a profile of the typical successful and unsuccessful students are identified. The empirical results show the following: (i) the most important factors separating successful from unsuccessful students are: ethnicity, course programme and course block; (ii) among classification tree growing methods Classification and Regression Tree (CART) was the most successful in growing the tree with an overall percentage of correct classification of 60.5%; and (iii) both the risk estimated by the cross-validation and the gain diagram suggests that all trees, based only on enrolment data are not quite good in separating successful from unsuccessful students. The implications of these results for academic and administrative staff are discussed.
    Citation
    Kovacic, Z. (2010). Early prediction of student success: Mining students' enrolment data. In Informing Science + Information Technology Education Joint Conference, Cassino, Italy.
    URI
    http://hdl.handle.net/11072/646
    Collections
    • Information Systems and Technology

    Browse

    All of Open Polytechnic RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Admin Login

    Statistics

    View Usage Statistics

    DSpace software copyright © 2002-2023  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV