Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach
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 stu...
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2020
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my.uniten.dspace-132122020-03-17T05:10:08Z Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach Ab Ghani, N.L. Che Cob, Z. Mohd Drus, S. Sulaiman, H. 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 naïve 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. 2020-02-03T03:31:08Z 2020-02-03T03:31:08Z 2019 Conference Paper 10.1007/978-3-030-20717-5_6 en |
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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 naïve 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. |
| format |
Conference Paper |
| author |
Ab Ghani, N.L. Che Cob, Z. Mohd Drus, S. Sulaiman, H. |
| spellingShingle |
Ab Ghani, N.L. Che Cob, Z. Mohd Drus, S. Sulaiman, H. Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| author_facet |
Ab Ghani, N.L. Che Cob, Z. Mohd Drus, S. Sulaiman, H. |
| author_sort |
Ab Ghani, N.L. |
| title |
Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| title_short |
Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| title_full |
Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| title_fullStr |
Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| title_full_unstemmed |
Student Enrolment Prediction Model in Higher Education Institution: A Data Mining Approach |
| title_sort |
student enrolment prediction model in higher education institution: a data mining approach |
| publishDate |
2020 |
| _version_ |
1662758830789885952 |
| score |
13.211869 |