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Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach

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dc.contributor.authorAb Ghani N.L.en_US
dc.contributor.authorChe Cob Z.en_US
dc.contributor.authorMohd Drus S.en_US
dc.contributor.authorSulaiman H.en_US
dc.contributor.authorid56940219600en_US
dc.contributor.authorid25824919900en_US
dc.contributor.authorid56330463900en_US
dc.contributor.authorid54903312800en_US
dc.date.accessioned2023-05-29T07:30:24Z
dc.date.available2023-05-29T07:30:24Z
dc.date.issued2019
dc.descriptionData Analytics; Decision trees; Education computing; Forecasting; Regression analysis; Students; Trees (mathematics); 10-fold cross-validation; Decision tree modeling; Educational data mining; Higher education institutions; Higher learning institutions; Logistic regressions; Predictive models; Student enrolment; Data miningen_US
dc.description.abstractThis paper demonstrates the application of educational data mining in predicting applicant�s enrollment decision for academic programme in higher learning institution. This research specifically aims to address the application of data mining on higher education institution database to understand student enrolment data and gaining insights into the important factors in making enrollment decision. By adapting the five phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) process model, detail explanations of the activities conducted to execute the data analytics project are discussed. Predictive models such as logistic regression, decision tree and na�ve bayes were built and applied to process the data set. Subsequently, these models were tested for accuracy using 10-fold cross validation. Results show that, given adequate data and appropriate variables, these models are capable of predicting applicant�s enrollment decision with roughly 70% accuracy. It is noted that decision tree model yields the highest accuracy among the three prediction models. In addition, different significant factors are identified for different type of academic programmes applied as suggested by the findings. � 2019, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-20717-5_6
dc.identifier.epage52
dc.identifier.scopus2-s2.0-85066119895
dc.identifier.spage43
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85066119895&doi=10.1007%2f978-3-030-20717-5_6&partnerID=40&md5=f817f9f97286dabf96702b5d037054ec
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25016
dc.identifier.volume565
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Electrical Engineering
dc.titleStudent Enrolment Prediction Model in Higher Education Institution: A Data Mining Approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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