International Journal of Advanced Trends in Computer Science and Engineering, Vol. 3 , No.1, Pages : 278 – 281 (2014) Special Issue of ICETETS 2014 - Held on 24-25 February, 2014 in Malla Reddy Institute of Engineering and Technology, Secunderabad– 14, AP, India 278 ISSN 2278-3091 Way to Learning Dropouts in Distance Education -A Study using Data mining Techniques B Deepthi Chary 1 , Y Sucharitha 2 , P Chandra shaker Reddy 3 1 Asst. Professor, Dept of CSE, Malla Reddy Inst. of Engg & Tech ,AP,INDIA, deepthi.chary@gmail.com 2 Asst. Professor, Dept of CSE, Malla Reddy Inst. of Engg & Tech ,AP,INDIA,suchi.yadala@gmail.com 3 Asst. Professor, Dept of CSE, Malla Reddy Engg. College,AP,INDIA,chandu.pundru@gmail.com ABSTRACT: Student dropout occurs quite often in universities providing distance education and the dropout rates are definitely higher than those in conventional universities. Limiting dropout is essential in university-level distance learning and therefore the ability to predict students, dropout could be useful in a great number of different ways. Distance education meets the needs of those students might otherwise be unable to attend on-campus classes, due to distance time constraints. One of the biggest benefit is the issue of flexibility and time. Because students are confined to a classroom for a certain number of hours on a given day , they can approach their coursework with flexibility and complete lessons when it suits their schedule. Data mining techniques are analytical tools that can be used to extract meaningful knowledge from large data sets. This paper focuses the on data mining in educational institution to extract useful information from the huge data sets on e-learning systems and environments for distance learning for such dropout students. Keywords – Data Mining, distance learning Drop-outs, prediction, coursework I .INTRODUCTION Today, a huge amount of data is available which can be used effectively to produce vital information. The information achieved can be used in the field of Medical science, Education, Business, Agriculture and so on. As huge amount of data is being collected and stored in the databases, traditional statistical techniques and database management tools are no longer adequate for analyzing this huge amount of data. Data Mining (sometimes called data or knowledge discovery) has become the area of growing significance because it helps in analyzing data from different perspectives and summarizing it into useful information. There are increasing research interests in using data mining in education. This new emerging field, called Educational Data Mining, concerns with developing methods that discover knowledge from data originating from educational environments. The data can be collected from various distance learning educational institutes that reside in their databases. The data can be personal or academic which can be used to understand students' behavior (socio economic factors for dropping out), to assist instructors, to improve teaching, to evaluate and improve e-learning systems, to improve their coursework. The objective is to identify the potential areas in distance education where data mining techniques can be applied in the field of Higher Education and e-learning activities by improving their knowledge skill development through continuing education Programs to meet their needs. II. DATA MINING DEFINITION AND TECHNIQUES Data mining (also known as Knowledge Discovery in Databases - KDD) has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move Beyond the analyses of past events provided by retrospective tools typical of decision support systems. The sequences of steps identified in extracting knowledge from data are: shown in Figure Knowledge