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Mining Student Academic Performance on ITE subjects using Descriptive Model Approach

Author Affiliations

  • 1College of Computer Studies, Laguna State Polytechnic University, Brgy. Bubukal Sta. Cruz, Laguna, Philippines

Res. J. Computer & IT Sci., Volume 4, Issue (11), Pages 1-15, November,20 (2016)


Data mining techniques has been useful to corporate world, however, it is transferable to educational environment. One of the objective of higher educational institution is to acquire potential alumni contributors. Secondary educational level echoes the educational value on college performance, the exact same reason the educational institution recognizes the significance of student admission, monitoring and evaluate potential students with good academic performance. Contemporary research paper on educational data mining focuses on applying data mining techniques in the context of student performance measurement and predictions. In this research, the descriptive model association and descriptive statistics cross tabulation is used to determine the subject area proficiencies of secondary educational institution and generate association rules using Predictive Apriori algorithm to discover hidden patterns. CRISP-DM methodology is used in this study to guide the researcher. The knowledge obtained from the model describes the subject area expertise of the high school as well as the subject ineptness of the school that would decision makers in student admission and academic planning of the educational institution.


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