Partitional Cluster for Educational Institution Quality Assessment

Abstract

The demand of data mining in educational institution has increased depending on the development of information systems. However, data mining needs high quality data while there are no sufficient methods to get quality data in the institutional zone for data mining to get a credible conclusion, which prevents its implementation. Commonly data cleaning and Extraction-Transformation-Loading tools or tolerance algorithms have been used to mine low quality data. But these methods only can improve the current data quality for mining, newly created data from institutional information system will make all data dirty again. By analyzing the reasons of low data quality on System Engineering theory, a new method called data mining consulting has been established, which solves the data quality problem by meta synthesis method including software designing, management and data mining testing, etc. Its application shows that it has good practicality which can do data mining project in low quality data and increase the institution’s decision levels. However, the main objective of an educational institution is to impart quality education. One way to reach the highest level of quality in education systems is by improving the decision making procedures on various processes such as assessment, evaluation, counseling and so on which requires knowledge. The proposal made in this research work evaluates the institutional quality in imparting education to the students using data mining techniques such as clusters, and predictive mining. It develops a clustering scheme to segment the student grades based on the knowledge acquired. In addition, it applies predictive mining with decision support tree mechanism to evaluate the quality of the institution with the hidden data attributes of the student profile, grade, faculty profile, curriculum, etc. extracted from educational data bases. The data mining evaluation provides the actual status on the institutional quality and guides the management in improving its performance by concentrating on the lacking areas. The system framework comprises clustering ensembles with the goal of creating an optimal framework for categorizing educational resources. It proposes both non adaptive and adaptive re-sampling schemes for the integration of multiple clustering (independent and dependent). Experimental results show improved stability and accuracy for clustering structures obtained via bootstrapping, sub sampling, and adaptive techniques. These improvements offer insights into specific decision within the data sets.