Modeling Student Performance in Higher Education Using Data Mining

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 524)

Abstract

Identifying students’ behavior in university is a great concern to the higher education managements (Kumar and Uma, Eur J Sci Res 34(4):526–534). This chapter proposes a new educational technology system for use in Knowledge Discovery Processes (KDP). We introduce the educational data mining (EDM) software and present the outcome of a test on university data to explore the factors having an impact on the success of the students based on student profiling. In our software system all the tasks involved in the KDP are realized together. The advantage of this approach is to have access to all the functionalities of the Structured Query Language (SQL) Server and the Analysis Services through a single developed software item, which is specific to the needs of a higher education institution. This model (Guruler et al., Comput Educ 55(1):247–254) aims to help educational organizations to better understand the KDPs, and provides a roadmap to follow while executing whole knowledge projects, which are nontrivial, involve multiple stages, possibly several iterations.

Keywords

Educational data mining Educational technology system and architectures Student relationship management Knowledge discovery software Decision tree 

Abbereviations

CM

Correlation matrices

DBMS

Database management system

DM

Data mining

DT

Decision tree

DTS

Data transformation services

EDM

Educational data mining

GPA

Grade point average

KDD

Knowledge discovery in databases

KDP

Knowledge discovery process

MDAC

Microsoft data access components

MDT

Microsoft decision tree

OLAP

On-line analytical processing

PDCA

Plan-do-check-act

SKDS

Student knowledge discovery software

SRM

Student relationship management

SQL

Structured query language

References

  1. 1.
    Abdous, M., He, W., Yen, C.J.: Using data mining for predicting relationships between online question theme and final grade. J. Educ. Technol. Soc. 15(3), 77–88 (2012)Google Scholar
  2. 2.
    Campagni R., Merlini D., Sprugnoli R.: Analyzing paths in a student database. In: Yacef, K., Zaïane, O., Hershkovitz, A., Yudelson, M., Stamper, J. (eds.) 5th International Conference on Educational Data Mining, pp. 208–209. International Educational Data Mining Society, Chania (2012)Google Scholar
  3. 3.
    Oyelade, O.J., Oladipupo, O.O., Obagbuwa, I.C.: Application of k-means clustering algorithm for prediction of students’ academic performance. Int. J. Comput. Sci. Inf. Secur. 7(1), 292–295 (2010)Google Scholar
  4. 4.
    Scheuer, O., McLaren, B.M.: Educational DM. In: Seel, N.M. (ed.) Encyclopedia of the Sciences of Learning. Springer, New York (2011)Google Scholar
  5. 5.
    Bidgoli B.M., Kashy D.A., Kortemeyer G., Punch W.F.: Predicting student performance: an application of DM methods with an educational web-based system. In: 33rd ASEE/IEEE Frontiers in Education Conference, pp. 13–18. IEEE, Boulder (2003)Google Scholar
  6. 6.
    Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C 40(6), 601–618 (2010)CrossRefGoogle Scholar
  7. 7.
    Delavari, N., Amnuaisuk, S.P., Beikzadeh, M.R.: DM application in higher learning institutions. Inform. Educ. 7(1), 31–54 (2008)Google Scholar
  8. 8.
    Vialardi, C., Chue, J., Peche, J.P., Alvarado, G., Vinatea, B., Estrella, J., Ortigosa, A.: A data mining approach to guide students through the enrollment process based on academic performance. User Model. User-Adap. Inter. 21(1–2), 217–248 (2011)CrossRefGoogle Scholar
  9. 9.
    Kumar, N.V.A., Uma, G.V.: Improving academic performance of students by applying data mining technique. Eur. J. Sci. Res. 34(4), 526–534 (2009)Google Scholar
  10. 10.
    IBM Case Study, Hamilton County Department of Education: Improving student performance and school effectiveness with predictive analytics. http.//www.ibm.com/analytics/us/en/case-studiesGoogle Scholar
  11. 11.
    IBM Case Study, Seton Hall University: Social media marketing analytics helps engage incoming prospects and increase enrollment yield http://www.ibm.com/analytics/us/en/case-studies
  12. 12.
    Cios, K.J., Swiniarski, R.W., Pedrycz, W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach, pp. 9–24. Springer, New York (2007)CrossRefGoogle Scholar
  13. 13.
    Meints, M., Möller, J.: Privacy preserving data mining: a process centric view from a European perspective. Report of the project FIDIS (Future of Identity in the Information Society), http://www.fidis.net/fileadmin/journal/issues/1-2007/Privacy_Preserving_Data_Mining.pdf
  14. 14.
    Jalili, M., Rezaie, K.: Quality principles deployment to achieve strategic results. Int. J. Bus. Excellence 3(2), 226–259 (2010)CrossRefGoogle Scholar
  15. 15.
    Maimon, O., Rokach, L.: Introduction to knowledge discovery and data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 1–15. Springer, New York (2010)CrossRefGoogle Scholar
  16. 16.
    Micić, Ž., Micić, M., Blagojević, M.: ICT Innovations at the platform of standardisation for knowledge quality in PDCA. Comput. Stand. Interfaces 36(1), 231–243 (2013)Google Scholar
  17. 17.
    Guruler, H., Istanbullu, A., Karahasan, M.: A new student performance analyzing system using knowledge discovery in higher educational databases. Comput. Educ. 55(1), 247–254 (2010)CrossRefGoogle Scholar
  18. 18.
    Guruler, H., Karahasan, M., Istanbullu, A.: Determining profile of university students: a case study on Mugla University databases. Mugla Univ. J. Soc. Sci. 18, 27–37 (2007)Google Scholar
  19. 19.
    Larson, B., English, D., Purington, P.: Delivering Business Intelligence with Microsoft SQL Server 2012. McGraw-Hill, New York (2012)Google Scholar
  20. 20.
    The Cyber Security and Information Systems Information Analysis Center (CSIAC): A Comparison of Leading DM Tools. https://sw.thecsiac.com/databases/url/key/222/225
  21. 21.
    Chikalov, I., Lozin, V., Lozina, I., Moshkov, M., Nguyen, H.S., Skowron, A., Zielosko, B.: Logical analysis of data: theory, methodology and applications. In: Chikalov, I., Lozin, V., Lozina, I., Moshkov, M., Nguyen, H.S., Skowron, A., Zielosko, B. (eds.) Three Approaches to Data Analysis, pp. 147–192. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  22. 22.
    Khan, D.M., Mohamudally, N., Babajee, D.K.R.: A unified theoretical framework for data mining. Procedia Comput. Sci. 17, 104–113 (2013)CrossRefGoogle Scholar
  23. 23.
    Özekes, S.: Classification and prediction in data mining with neural networks. Istanbul Univ. J. Electr. Electron. Eng. 3(1), 707–712 (2012)Google Scholar
  24. 24.
    Cano, A., Zafra, A., Ventura, S.: An interpretable classification rule mining algorithm. Inf. Sci. 240, 1–20 (2013)Google Scholar
  25. 25.
    Guan, H.: A new data mining approach combining with extension transformation of extenics. In: Deng, W. (ed.) Future Control and Automation, vol. 173, pp. 199–205. LNEESpringer, Heidelberg (2012)CrossRefGoogle Scholar
  26. 26.
    Lakshmi, T.M., Martin, A., Begum, R.M., Venkatesan, V.P.: An analysis on performance of decision tree algorithms using student’s qualitative data. Int. J. Mod. Educ. Comput. Sci. 5(5), 18–27 (2013)CrossRefGoogle Scholar
  27. 27.
    Lin, C.F., Yeh, Y.C., Hung, Y.H., Chang, R.I.: Data mining for providing a personalized learning path in creativity: an application of decision trees. Comput. Educ. 68, 199–210 (2013)CrossRefGoogle Scholar
  28. 28.
    James, G., Witten, D., Hastie, T., Tibshirani, R.: Tree-based methods. In: Casella, G., Fienberg, S., Olkin, I. (eds.) An Introduction to Statistical Learning, vol. 41, pp. 303–335. Springer, New York (2013)CrossRefGoogle Scholar
  29. 29.
    Yang, H., Fong, S.: Optimized very fast decision tree with balanced classification accuracy and compact tree size. In: 3rd International Conference on Data Mining and Intelligent Information Technology Applications, pp. 57–64. IEEE, Coloane (2011)Google Scholar
  30. 30.
    López-Chau, A., Cervantes, J., López-García, L., García Lamont, F.: Fisher’s Decision Tree. Expert Systems with Applications 40(16), 6283–6291 (2013)Google Scholar
  31. 31.
    Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 163–222. Springer, New York (2012)CrossRefGoogle Scholar
  32. 32.
    Microsoft Decision Trees Algorithm Technical Reference http://msdn.microsoft.com/en-us/library/cc645868.aspx
  33. 33.
    Prati, R.C., Batista, G.E.A.P.A., Monard, M.C.: A survey on graphical methods for classification predictive performance evaluation. IEEE Trans. Knowl. Data Eng. 23(11), 1601–1618 (2011)CrossRefGoogle Scholar
  34. 34.
    Data Mining Algorithms (Analysis Services - Data Mining) http://msdn.microsoft.com/en-us/library/ms175595.aspx

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information Systems Engineering, Technology FacultyMugla Sitki Kocman UniversityKötekli, MuglaTurkey
  2. 2.Department of Computer Engineering, Engineering and Architecture FacultyBalikesir UniversityCagiş, BalikesirTurkey

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