Student Data Analysis with RapidMiner Jovica Krstevski 1 , Dragan Mihajlov 2 , Ivan Chorbev 3 1 Innovaworks. ul.Partizanski Odredi 73, MK-1000 Skopje, Republic of Macedonia jovicakrstev@yahoo.com 2,3 Faculty of Electrical Engineering and Information Technologies, PO Box 574, Skopje, Republic of Macedonia {dragan, ivan}@feit.ukim.edu.mk Abstract. The aim of this paper is to propose a methodology for modeling and implementing algorithms over data from student management systems with RapidMiner. RapidMiner is an environment for business analytics, predictive analytics, data mining, text mining, and machine learning. Traditional educational systems collect a lot of data about students. The collected data can be used to extract information and experiences. The extracted information can help school managers prepare better curriculum and manage the schools in an informed manner. Teachers can have advanced information about student progress and the potential weaknesses in courses. In this paper we describe how tools like RapidMiner can be used to extract information from raw student data. We have used student data from the Macedonian educational system, compared different algorithms and chose the most appropriate for the given model. Keywords: Student Analysis, Academic Analytic, Educational Data Mining, RapidMiner. 1 Introduction We are witnesses of how modern world education plays a significant role in people’s lives. The growing need for an educated workforce in the society requires education to be a leader in the reform of the economy. During the last decades an increasing number of educational organizations store their data in electronic format and the amount of stored information is increasing each day. In order to analyze collected data one needs a process where the data will be inspected, cleaned, transformed, and modeled. From the analysis one can extract information, propose conclusions and support decision making. Data analysis can be found in science, social and business domains. It has different approaches and includes versatile techniques with different names. This process in the educational area is called Educational Data Mining (EDM) [5], which converts student raw data from educational systems to information to help the educational process. ICT Innovations 2011 Web Proceedings ISSN 1857-7288 19 L. Kocarev (Editor): ICT Innovations 2011, Web Proceedings, ISSN 1857-7288 © ICT ACT – http://ictinnovations.org, Skopje, 2012