Education quality is the main concern of educational institutions and organizations. However, statistical data showed us that there is a big gap and challenges in quality education in terms of professional contribution to development, individual competence, and more specifically in students’ performance. One of the solutions to reduce the problem of education quality is finding the causes and analyze through research using Data mining analytical tools. Therefore, the aim of this study is to identify education quality determinant factors. taking the domain 8520 Amhara region primary and secondary schools performance evaluation data of the years 2006-2008. we applied the J48 decision tree and JRip rule induction algorithms using WEKA data mining tool to build different models that identify the most determinant factors for education quality. After preprocessing a total of 8514 records are used for building the models and experiments are made to come up with a meaningful output. Major factors of quality education are identified and rules are generated using J48 decision trees and JRip rule induction algorithm with 84.67% and 84.80% accuracy respectively. The comparison of the models using WEKA's experimenter showed that JRip algorithm outperforms J48 algorithm. The most determinant factors for quality education identified by JRip algorithm includes: Teaching Learning Facilities, Financial Efficiency for Improvement, Student Participation, Teachers Education Delivery Performance, Students Sentiment, Responsibility and Behavior. Finally the finding of this thesis work could be a reference document for experts, decision makers and researchers who are interested in the field.