Educational Institutional Quality Assessment Using Cluster and Predictive Data Mining Models

The most important facts in educational institutional system growth lies in the quality of services rendered. It concerns with all the circumstances i.e., faculty profile, student performance and infrastructure requirements that allow decision makers to better evaluate and improve the institutional performance. The highest level of quality in educational institution can be achieved by improving the decision making procedures on the various processes such as planning, counseling, evaluation and so on. This can be achieved by utilizing the managerial decision makers with valuable implicit knowledge, which is currently unknown to them. This knowledge is hidden among the educational data set and it is extractable through data mining technology. The proposed work presented a fuzzy k-means cluster algorithm in the formation of student, faculty and infrastructural clusters based on the performance, skill set and facilitation availability respectively. With the obtained data clusters, quality assessment is made by predictive mining using decision tree model. Experimental results show improved stability and accuracy for clustering structures obtained via sub sampling, and adaptive techniques. These improvements offer insights into specific decision within the data sets. The proposal also works in the direction to provide accurate up-to-date and accessible information on performance of education institutions to enable stakeholders to make informed choices. The quality evaluation provides the management with bases for policy options on education and informed decisions for development assistance and incentives to its educational system.