Data mining to predict the performance of the staffs in elementary schools
Publish place: سومین کنفرانس بین المللی مدیریت در قرن 21
Publish Year: 1395
Type: Conference paper
Language: English
View: 797
This Paper With 8 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
Export:
Document National Code:
ICMNG03_159
Index date: 15 December 2016
Data mining to predict the performance of the staffs in elementary schools abstract
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.
Data mining to predict the performance of the staffs in elementary schools Keywords:
Data mining to predict the performance of the staffs in elementary schools authors
Reza Samizadeh
Faculty member of Industrial Engineering, Alzahra University, Tehran, Iran
Solaleh Sadat Kalantari Kohbanani
PhD student of Industrial Engineering, Alzahra University, Tehran, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :