Reza Samizadeh - Faculty member of Industrial Engineering, Alzahra University, Tehran, Iran
Solaleh Sadat Kalantari Kohbanani - PhD student of Industrial Engineering, Alzahra University, Tehran, Iran

One of the most important elements to promote the level of an educational system is inviting staff with a proper and acceptable performance. Hence, predicting staff ` performance is critical. There are many valuable researches in educational data mining. But they concentrate on predicting students more than staff. Therefore this study addresses the predicting performance of the staffs in elementary schools. We apply data mining tools to do the mentioned object. Nine variables which are seems to have more contribution on staff` s performance are considered and then results related in classification coming through decision tree and clustering from Simple K-Means clustering algorithm are presented. Furthermore Apriori algorithm is used to discover explore association rules. Finally it can be found that degree of education, The relationship between one`s field of study and his career duties and reading daily time are more noticeable parameters to have a higher performance.

Performance evaluation, data mining, school personnel