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

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Ab Ghani N.L.
Che Cob Z.
Mohd Drus S.
Sulaiman H.
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Springer Verlag
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This 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.
Data 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 mining