Advantages Of Educational Data Mining

The increasing interest in educational data mining makes it a new growing research area. Educational data mining also referred to as “EDM” is defined as the area of scientific inquiry centered around the development of techniques for making discoveries within the sets of data found in learning environment, and using those techniques to understand the students and the learning environment. It is a very powerful tool to reveal hidden patterns and precious knowledge, which otherwise may be difficult to establish and comprehend using traditional statistical methods. This paper introduces educational data mining, its advantages and limitation. discusses educational data mining techniques by conducting step by step processes also identified the

Learners are engaged in educational content in determining optimal instructional sequences in order to support the learning style of students

8) Administrators to understand the best way to extend the institutional resources both human and material for the overall development of educational system [4].

D. Limitation of Data Mining The research trend of Educational Data Mining shows that maximum research focuses only on academic objectives. The other issues are: [5].

• Incremental nature of educational data: The exponential increase of data makes the maintenance of the data in data warehouse very difficult. Also in the monitoring of the operational data sources, infer the student interest, intentions and its impact in a particular institution is the main issue. There is also the issue of the alignment and interpretation of the incremental data. It should focus on appropriating time, context and its sequence. Another issue of incremental is the optimal utilization of human computing and resources [5].

• Occurrence of Uncertainty: The presence of uncertain errors, no model can accurately give a prediction of hundred percent results of student model or the general academic planning

E. Phases in Educational Data Mining

Phases of Educational Data Mining Educational Data Mining are concerned with translation of raw data collected from educational systems to discovering a new hidden information. Generally EDM consist of following phases, as shown in fig.3 below

 Data collection phase : The data to be mined is collected from different educational system resources i.e. from course management, E-learning environment, web based data (i.e. YouTube, twitter)which is relevant to students activities during learning process like their academic grades, students posts on social networking sites etc

 Data Mining Phase: The data mining phase consist of the following process phases

1) Discover Relationship: The first phase of educational data mining is to find the relationships between the data of educational environment using data mining techniques i.e. classification, clustering, regression etc. with the objective of finding consistent relationships between data variables

2) Validating Relationships in the second phase of educational data mining, validation of discovered relationships between data are done so that uncertainty can be avoided and in order to avoid over