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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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  • N. Delavari, M. R. A. Shirazi, M. R. Beikzadeh. A New Model for Using Data Mining in Higher Educational System, 5th International Conference on Information Technology based Higher Education and Training: ITEHT ’04, Istanbul, Turkey, 31st May-2nd Jun 2004.

  • J. Han, How can Data Mining Help Bio-Data Analysis. BIOKDD02: Workshop on data mining in Bioinformatics, 2002.

  • R. Feldman, Mining the Biomedical Literature using Semantic Analysis and Neural Language Processing Techniques, a link analysis approaches. Clear Forest Corporation, New York, 2003.

  • H. Edelstein, Building Profitable Customer Relationships with Data Mining", Two Crows Corporation, SPSS white paper-executive briefing, 2000.

  • W. H. T. Chang and Y. H. Lee, Telecommunications Data Mining for Target Marketing, Journal of Computers, Vol. 12, No. 4, December 2000, pp.60-74.

  • J. Hans and M. Kamber. Data Mining: Concepts and Techniques. Simon Fraser University, Morgan Kaufmann publishers, ISBN 1-55860-489-8. 2001.

  • M. Reza and N. Delavari, Processes in Higher Education System, TS3B-2 M2 USIC 2004.

  • Information Technology journal 5(3): ISSN 1812-5638, Data mining for better higher education system.

  • Journal of Data Science 8(2010) Data mining approach for Identifying Predictors of Student Retention from sophomore to Junior Year.

  • Ryan S.J.D. Baker, The State of Educational Data Mining in 2009: A Review and Future Visions [pp].

  • Academic Analytics and Data Mining in Higher Education www.academics.georgiasouthern.edu/ijsotl/v4n2/.../PDFs/_BaeplerMurdoch.pdf.

  • Aziz, W. Rizhan and H. Hassan “Intelligent System for Personalizing Students’ Academic Behaviors” International Journal on New Computer Architectures and Their Applications (IJNCAA) 2(1): 138-153 The Society of Digital Information and Wireless Communications, 2012 (ISSN: 2220-9085).

  • Cognos, The right architecture for business intelligence, CognosInc: Burlington, USA, 2008.

  • Jason, O., W., K., Kannan, S., and Asirvadam, V., S.: Data Mining and Warehousing Approaches on School Smart System: A Conceptual Framework, In Proceedings of Knowledge Management International Conference, pp. 20-24 (2008).

  • Galit.et.al. 2007.”Examining online learning processes based on log files analysis” : a case study. Research, Reflection and Innovations in Integrating ICT in Education.

  • Madhyastha.T.and Tanimoto, S., 2009.” Student Consistency and Implications for Feedback in Online Assessment Systems. “In Proceedings of the 2nd International Conference on Educational Data Mining, pp81-90.

  • Z. N. Khan, Scholastic Achievement of Higher Secondary Students in Science Stream, Journal of Social Sciences, Vol. 1, No. 2, 2005, pp84-87.

  • J. A. Moriana, F. Alos, R. Alcala, M. J. Pino, J. Herruzo, and R. Ruiz, Extra-Curricular Activities and Academic Performance in Secondary Students, Electronic Journal of Research in Educational Psychology,Vol. 4, No. 1, 2006, pp35-46.

  • P. Cortez, and A. Silva, Using Data Mining To Predict Secondary School Student Performance, In EUROSIS, A. Brito and J. Teixeira (Eds.), 2008, pp5-12.

  • G.Paul Suthan and Dr. Santhosh Baboo Hybrid CHAID a key for MUSTAS Framework in Educational Data Mining, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 1, January 2011 ISSN (Online): 1694-0814].

  • M. Ramaswami and R. Bhaskaran. A CHAID Based Performance Prediction Model in Educational Data Mining, Madurai Kamaraj University, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 1, No. 1, January 2010.


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  • A Proposed EDM Framework for Improving Student Performance

Abstract Views: 300  |  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