Multiple educational data mining approaches to discover patterns in university admissions for program prediction

Julius Cesar O. Mamaril, Melvin A. Ballera


This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.


Attribute selection; Design algorithm; Educational data mining; Pattern discovery; Program prediction; Supervised learning; University admission

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