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Analyzing the College Freshmen Aptitude Results using K-means Algorithm

Universities today are operating in a very complex and highly competitive environment. Huge data available in the educational field bring out the hidden knowledge which is helpful in decision making. Cagayan State University used Rapidminer as an educational data mining (EDM) tool to extract new knowledge from huge standardized aptitude result of 3,393 data stored in an Excel database. Clustering algorithms such as K-Means where k=3 and K-means where k=4 number were used for evaluating students’ records run over the Rapidminer application to enable clustering prototype. The prototype was assessed and used internal validation using the silhouette index to classify which clustering algorithms work best. Finally, the study determined the degree of the students’ aptitude that can be used for advising students on the most appropriate course that they can take. The K-means algorithms using parameter k=4 was determined as the best parameter in the clustering.