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A Proposed EDM Framework for Improving Student Performance


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1 M.C.A Department, Ganpat University, India
     

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Educational Data Mining (EDM) is an emergent discipline for exploring data, and a method to support learning and teaching processes. In this paper, we proposed Educational Intelligence (EI) Framework by combining BI technologies with various EDM algorithm techniques. In last decade, the higher education in India has grown manifold. Private participation in establishing new institutions, encouraged by the government, forced the higher education to revisit their scope and objectives in the long run to sustain. This paper aim is to improve the efficiency of higher educational institutions by applying data mining techniques such as clustering, decision tree, association etc. This paper also describe that how data mining algorithms can be applied to higher education processes for enhancing student’s performance. From the analysis of this framework, the groups of students who have excellent skills or vice versa can be identified. It also optimizes the time to perform current and historical data analysis. The weaknesses and strengths of the student can also be obtained. Finally, students’ future potential areas of studies can be predicted using the framework.

Keywords

Business Intelligent, Educational Intelligence, Educational Data Mining, Educational Data Warehouse Introduction, Data Mining Algorithms, Business Intelligent.
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  • A Proposed EDM Framework for Improving Student Performance

Abstract Views: 388  |  PDF Views: 6

Authors

Bhavesh R. Patel
M.C.A Department, Ganpat University, India

Abstract


Educational Data Mining (EDM) is an emergent discipline for exploring data, and a method to support learning and teaching processes. In this paper, we proposed Educational Intelligence (EI) Framework by combining BI technologies with various EDM algorithm techniques. In last decade, the higher education in India has grown manifold. Private participation in establishing new institutions, encouraged by the government, forced the higher education to revisit their scope and objectives in the long run to sustain. This paper aim is to improve the efficiency of higher educational institutions by applying data mining techniques such as clustering, decision tree, association etc. This paper also describe that how data mining algorithms can be applied to higher education processes for enhancing student’s performance. From the analysis of this framework, the groups of students who have excellent skills or vice versa can be identified. It also optimizes the time to perform current and historical data analysis. The weaknesses and strengths of the student can also be obtained. Finally, students’ future potential areas of studies can be predicted using the framework.

Keywords


Business Intelligent, Educational Intelligence, Educational Data Mining, Educational Data Warehouse Introduction, Data Mining Algorithms, Business Intelligent.

References