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Modeling Student Performance in Higher Education Using Data Mining

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Educational Data Mining

Part of the book series: Studies in Computational Intelligence ((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.

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Abbreviations

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. 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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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-studies

    Google Scholar 

  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. Cios, K.J., Swiniarski, R.W., Pedrycz, W., Kurgan, L.A.: Data Mining: A Knowledge Discovery Approach, pp. 9–24. Springer, New York (2007)

    Book  Google Scholar 

  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. Jalili, M., Rezaie, K.: Quality principles deployment to achieve strategic results. Int. J. Bus. Excellence 3(2), 226–259 (2010)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Larson, B., English, D., Purington, P.: Delivering Business Intelligence with Microsoft SQL Server 2012. McGraw-Hill, New York (2012)

    Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  22. Khan, D.M., Mohamudally, N., Babajee, D.K.R.: A unified theoretical framework for data mining. Procedia Comput. Sci. 17, 104–113 (2013)

    Article  Google Scholar 

  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. Cano, A., Zafra, A., Ventura, S.: An interpretable classification rule mining algorithm. Inf. Sci. 240, 1–20 (2013)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  32. Microsoft Decision Trees Algorithm Technical Reference http://msdn.microsoft.com/en-us/library/cc645868.aspx

  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)

    Article  Google Scholar 

  34. Data Mining Algorithms (Analysis Services - Data Mining) http://msdn.microsoft.com/en-us/library/ms175595.aspx

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Authors and Affiliations

  1. Department of Information Systems Engineering, Technology Faculty, Mugla Sitki Kocman University, 48000, Kötekli, Mugla, Turkey

    Huseyin Guruler

  2. Department of Computer Engineering, Engineering and Architecture Faculty, Balikesir University, 10145, Cagiş, Balikesir, Turkey

    Ayhan Istanbullu

Authors
  1. Huseyin Guruler
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  2. Ayhan Istanbullu
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Correspondence to Huseyin Guruler .

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Editors and Affiliations

  1. Escuela Superior de Ingeniería Mecánica y Eléctrica, Zacatenco (ESIME-Z), World Outreach Light to the Nations Ministries (WOLNM), Instituto Politécnico Nacional (IPN), Gustavo A. Madero, Mexico City, Distrito Federal, Mexico

    Alejandro Peña-Ayala

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Guruler, H., Istanbullu, A. (2014). Modeling Student Performance in Higher Education Using Data Mining. In: Peña-Ayala, A. (eds) Educational Data Mining. Studies in Computational Intelligence, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-02738-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-02738-8_4

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  • Online ISBN: 978-3-319-02738-8

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