Clustering Based Analytical Review for Improving the Quality of Primary Education in MP

Abstract

Data mining is a powerful analytical tool that enables educational institutions to better allocate resources and staff, and proactively manage student outcomes. The management system can improve their policy, enhance their strategies and thereby improve the quality of that management system. Data mining techniques capabilities provided effective improving tools for student performance. Different clustering algorithms have been used to measure the performance of students such as hierarchical agglomerative clustering, K-means and model based clustering to identify groups of students with similar skill profiles a clustering algorithm based on large generalized sequences to find groups of students with similar learning characteristics based on their traversal path patterns. The main indicator of the quality of elementary education can be visualized in terms of its product like the learners achievement both in scholastic and co-scholastic areas i.e. the performance in various subjects of study and habits, attitudes, values and life skills necessary for becoming a good citizen. Quality issues in elementary education will revolve around the quality of infrastructure and support services, opportunity time, teacher characteristics and teacher motivation, pre-service and inservice education of teachers, curriculum and teachinglearning materials, classroom processes, pupil evaluation, monitoring and supervision etc.