Data mining model for a better higher educational system

被引：0
作者：
Department of Computer Science, Dr. Ambedkar Government Arts College, Chennai-600 039, India [1 ]
不详 [2 ]
机构：
[1] Department of Computer Science, Dr. Ambedkar Government Arts College
[2] Mohammed Sadak Trust, Group of Educational Institutions
来源：
Inf. Technol. J. | 2006年 / 3卷 / 560-564期
关键词：
Association; Classification; Data mining; Higher education;
D O I：
10.3923/itj.2006.560.564
中图分类号：
学科分类号：
摘要：
The main objective of any higher educational institution is to impart quality education. One way to reach the highest level of quality in higher education systems is by improving the decision making procedures on various processes such as assessment, evaluation, counseling and so on which requires knowledge. The knowledge is hidden among the educational data set and it is extractable through data mining technology. This paper is designed to present and justify the capabilities of data mining in the context of higher education by offering a data mining model for higher educational system in the colleges. It presents an approach to classifying students in order to predict their final grade based on certain features extracted from educational data bases. It helps earlier in identifying the dropouts and students who are below average and allow the teacher to provide appropriate counseling/advising in appropriate time. © 2006 Asian Network for Scientific Information.
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页码：560 / 564
页数：4
相关论文
共 11 条
  • [1] Adriaans P., Zantinge D., Data Mining, (2000)
  • [2] Antonio A.L., The influence of friendship groups on intellectual self-confidence and educational aspirations in college, J. Higher Education, 75, pp. 446-470, (2004)
  • [3] Beck J., Analyzing student-tutor interaction logs to improve educational outcomes, Proc. ITS 2004 Workshop, pp. 60-67, (2004)
  • [4] DesJardins S.L., Ahlburg D.A., McCall B.P., A temporal investigation of factors related to timely degree completion, J. Higher Education, 73, pp. 555-581, (2002)
  • [5] Han J., Kamber M., Data Mining: Concepts and Techniques, (2003)
  • [6] Minaei B., Kortemeyer G., Punch W.F., Association analysis for an online education system, IEEE International Conference on Information Reuse and Integration (IRI-2004), (2004)
  • [7] Murtaugh P.A., Burns L.D., Schuster J., Predicting the retention of university students, Res. Higher Education, 4, pp. 355-371, (1999)
  • [8] Silva D.R., Vieira M.T.P., An ongoing assessment model in distance learning, Proceedings of Internet and Multimedia Systems and Applications, (2001)
  • [9] Thomas C.G., Elbasyouni M.L., Student online assessment behaviors, IEEE Transactions on Education, 48, pp. 400-401, (2005)
  • [10] Webb N.M., Peer interaction and learning in small groups, Intl. J. Educational Res., 13, pp. 21-29, (2003)
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