Paper Title
Evaluating School Administrators’ Perspectives About Principal-Ship, Self-Efficacy and Decision Making By Using Association Rule

This paper aims to predict the relationship between questions askedto school administrators. The questions are based onbelieves in principal-ship, self-efficacy, and Melbourne decision-making. The data obtained about school administrators’ perspectives was evaluated by using Apriori algorithm of association rule under data mining technique.School administrators are individuals who obtained a certificate of bachelor degree or post graduate degree. They manage the routine activities and often to provide instructional leadership in schools. Their roles include supervising staffs, making decisions that are effective within the school premises, controlling the budget or expenses of the schools,and making sure that everything goes as expected. Data mining is a field comprising many different techniques that can be used to acquire better or new information from alarge amount of data. These techniques include Classification, Sequential Patterns, Clustering, Prediction, Association Rule, and Decision Trees. Nowadays, educationally making the right decision is a very important task.Because,whenever an educational decision is made will affect many individuals’ lives.Therefore, well trained and educated individuals mean a better living society or community. Firstly, Apriori algorithm is applied to the questionnaire of 57 participants, and in order to predict the relationship between the questions 100,000 rules aregenerated.The rules with theconfidence of 100 percent are extracted, and the best rules are obtained based on the demographical information. Lastly, after examining the results with highest confidence value some interesting outcomes are noticed. For all of the rules,the lift values appeared to be higher than 1, and most of the results show that the relationships are part-based of the questionnaire while only afew of the results are parts-mixed-based. Keywords - School Administrators; Data mining; Association rule.