# Development of College Completion Model Based on K-means Clustering Algorithm

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3 results based on cluster analysis and used standard statistical algorithms to arrange their scores according to their level of performance. K-means clustering algorithm was implemented. The model created was an improvement of the limitation of existing methods developed by Omelehin using fuzzy logic. Reference [13] in their study presented a hybrid procedure based on decision tree of data mining method and data clustering which will enable academicians to predict student s GPA and based on that, instructors can take a necessary step to improve student academic performance. C. Methodology/Data Mining Process The data mining tool used in this study is Weka which offers different data mining techniques for various kinds of data. The Weka Knowledge Explorer is an easy to use graphical user interface that harnesses the power of the Weka software. The major Weka packages are Filters, Classifiers, Clusters, Associations, Attribute Selection and Visualization tool, which allows datasets and the predictions of Classifiers and Clusters to be visualized in two or three dimensions. The workbench contains a collection of visualization tools and algorithms for data analysis and predictive modeling together with graphical user interfaces for easy access to this functionality. Weka was primarily designed as a tool for analysing data from agricultural domains. It is now used in many different application areas, in particular for educational purposes and research [15]. K-means clustering technique was selected to analyze the dataset extracted from the Student Information and Accounting System of the Isabela State University. Fig. 3 shows the process of how k-means clustering work. A clustering algorithm attempts to find natural groups of components (or data) based on some similarity. Also, the clustering algorithm finds the centroid of a group of data sets. To determine cluster membership, most algorithms evaluate the distance between a point and the cluster centroids. The output from a clustering algorithm is basically a statistical description of the cluster centroids with the number of components in each cluster. III. WORK DONE/CONTRIBUTION A. Framework of the Study The framework of the study was based on the Knowledge Discovery Process (KDP) illustrated by [14]. The KDP figure was modified to suit the objectives of the study. The modified version was presented on Fig. 1 following the steps from preprocessing wherein noisy and irrelevant data were removed, selection and transformation where data relevant to the analysis task were retrieved from the database and further transformed or consolidated into forms appropriate for mining, data mining where k-means clustering were applied in order to extract data patterns, interpretation and evaluation where the truly interesting patterns representing knowledge based were identified and knowledge presentation where visualization and knowledge presentation techniques were used to present the mined knowledge to the user. Interpretation & Evaluation Knowledge Data Mining Selection & Transformation K-means Clustering Training/ Test Dataset Preprocessing appropriate dataset for the study. The dataset was produced after data pre-processing. This served as input to the data mining tool for the application of the selected k-means clustering algorithms to develop the college completion model. Two clusters were produced after the process. The model was evaluated based from their accuracy consistent with the results obtained from training dataset. Cluster number represents groups of student related or similar with each other. The knowledge discovered can then be used for decision making. Student Academic Records Student Information System Fig. 1. The steps of extracting knowledge from data. Student Related Attributes - HSGPA - CET - Gender - Scholarship - GPA1st 1stsem - GPA1st 2ndsem K-means Clustering Data Mining Technique College Completion Model - Cluster 0 - Cluster 1 Fig. 2. College completion model framework using K-means clustering algorithm technique. B. College Completion Model Process Framework Data mining is just a part of the whole framework of the study. Fig. 2 shows the college completion process framework as its major components used in this study. The information stored in the Student Information and Accounting System were analysed to be able to extract Fig. 3. K-means clustering process flowchart. Fig. 4 shows that 392 out of 1053 students or 37.23% completed their enrolled program on time. The rest of the students are either dropped or still enrolled to complete the program. 174

6 Allen M. Paz was born in Taysan, Batangas, Philippines on October 11, She took her undergraduate program from Adamson University, Manila, Philippines, with a degree of bachelor of science in computer engineering in She finished her master s degree in information technology from the University of La Salette, Isabela, Philippines in She is currently taking her doctor in information technology at the Technological Institute of the Philippines, Quezon City. She is presently working at the Isabela State University as a faculty member with a rank of assistant professor II. She was designated department chairman of the Information and Communications Technology Department from 2003 to 2010 in the College of Development Communication and Arts & Sciences of Isabela State University, Philippines. She is presently working on her research in data mining. Prof. Paz is a member of International Association of Computer Science and Information Technology (IACSIT), International Association of Engineers (IAENG), Philippine Society of IT Educators (PSITE) and Isabela State University Faculty Association. She is an active member of Accrediting Agency of Chartered Colleges and Universities of the Philippines (AACCUP). Bobby D. Gerardo is currently the vice president of Administration and Finance of West Visayas State University, Iloilo City, Philippines. His dissertation is Discovering driving patterns using rule-based intelligent data mining agent (RiDAMA) in distributed insurance telematic systems. He has published 54 research papers in national and international journals and conferences. He is a referee of international conferences and journal publications such as IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Knowledge and Data Engineering. He is interested in the following research fields: distributed systems, telematics systems, CORBA, data mining, web services, ubiquitos computing and mobile communications. Dr. Gerardo is a recipient of CHED Republica Award in Natural Science Category (ICT field) in His paper entitled SMS-based automatic billing system of household power consumption based on active experts messaging was awarded best paper on December 2011 in Jeju, Korea. Another best paper award for his paper was Intelligent decision support using rule-based agent for distributed telematics systems, presented at the Asia Pacific International Conference on Information Science and Technology, on December 18, An excellent paper award was given for his paper Principal component analysis mechanism for association rule mining, on Korean Society of Internet Information s (KSII) 2004 Autumn Conference on November 5, He was given a university researcher award by West Visayas State University in Bartolome T. Tanguilig III was born on February 24, 1970 in Baguio City, Philippines. He took his bachelor of science in computer engineering in Pamantasan ng Lungsod ng Maynila, Philippines in He finished his master degree in computer science from De la Salle University, Manila, Philippines in His doctor of philosophy in technology management was awarded by the Technological University of the Philippines, Manila in He is currently the assistant vice president of Academic Affairs and concurrent dean of the College of Information Technology Education and Graduate Programs of the Technological Institute of the Philippines, Quezon City. His research entitled J-master: an interactive game-based tool for teaching and learning basic java programming was awarded the best research in the 10 th National Convention for IT Education held in Ilocos Norte, Philippines in He published a research entitled Predicting faculty development trainings and performance using rule-based classification algorithm in Asian Journal for Computer Science and Information Technology. Dr. Tanguilig is a member of Commission on Higher Education Technical Panel for IT Education, Board Chairman of Junior Philippine IT Researchers, member of Computing Society of the Philippines and Philippine Society of IT Educators-NCR. 177

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