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

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dc.contributor.authorKovacic, Z.
dc.date.accessioned2012-09-17T01:49:11Z
dc.date.available2012-09-17T01:49:11Z
dc.date.issued2010
dc.description.abstractThis 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.
dc.identifier.citationKovacic, Z. (2010). Early prediction of student success: Mining students' enrolment data. In Informing Science + Information Technology Education Joint Conference, Cassino, Italy.
dc.identifier.urihttp://hdl.handle.net/11072/646
dc.language.isoen
dc.subjectStudy outcomes
dc.subjectDistance learning
dc.subject.other280000 Information, Computing and Communication Sciences
dc.titleEarly prediction of student success: Mining students' enrolment data.
dc.typeConference Paper
opnz.createdConference held 19-24 June.
opnz.formatInforming Science + Information Technology Education Joint Conference
opnz.hasVersionCassino, Italy
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