CASIRJ Volume 6 Issue 1 [Year - 2015] ISSN 2319 – 9202 International Research Journal of Commerce Arts and Science http://www.casirj.com Page 309 Developments in Educational Data mining PARVESH KUMAR, Govt. College for women, Mokhra (Rohtak) Haryana (India) Email: parveshiitd@gmail.com Abstract:- Educational Data Mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to understand the students better. In this paper, we studied the developments in the field of Educational Data Mining. Keywords: Data mining , KDD, EDM Introduction to Data mining: Knowledge discovery in databases (KDD) is an independent research discipline which discovers information from large amount of data. KDD is a series of processes including data collection, data pre-processing, data transformation, data mining and knowledge presentation. Among these processes, data mining is a vital step. Data mining is the process of extracting non-trivial, implicit, previously unknown and potentially useful information or patterns from large data repositories using applications of special algorithms built upon sound principles from numerous disciplines including statistics, artificial intelligence, machine learning, database science, and information retrieval. Descriptive mining and Predictive mining are the two classes of data mining generally. A Predictive mining forecasts about the values of the data using known results found from different data. Predictive data mining tasks include classification, regression and time-series. Descriptive mining is a process to summarize or characterize general properties of data in data repository. Unlike Predictive mining, a Descriptive mining serves as a way to explore the properties of the data examined, not to predict new properties. Clustering, summarization, association rules and sequence discovery are usually considered as descriptive in nature.