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Data Mining Approach For Predicting Student Performance

Author

Listed:
  • Edin Osmanbegovic

    (University of Tuzla, Faculty of Economics)

  • Mirza Suljic

    (University of Tuzla, Faculty of Economics)

Abstract

Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaluating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.

Suggested Citation

  • Edin Osmanbegovic & Mirza Suljic, 2012. "Data Mining Approach For Predicting Student Performance," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 10(1), pages 3-12.
    • Handle: RePEc:tuz:journl:v:10:y:2012:i:1:p:3-12
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    Citations

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    Cited by:

    1. Anjeela Jokhan & Aneesh A. Chand & Vineet Singh & Kabir A. Mamun, 2022. "Increased Digital Resource Consumption in Higher Educational Institutions and the Artificial Intelligence Role in Informing Decisions Related to Student Performance," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    2. Bilal Zorić, Alisa, 2019. "Predicting Students’ Success Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 58-66, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    3. Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(3), pages 38-49, July.
    4. Alisa Bilal Zorić, 2020. "Predicting Students’ Academic Performance Based on Enrolment Data," International Journal of Innovation and Economic Development, Inovatus Services Ltd., vol. 6(4), pages 54-61, October.
    5. January D. Febro & Jocelyn Barbosa, 2017. "Mining student at risk in higher education using predictive models," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 3(4), pages 117-132.

    More about this item

    Keywords

    data mining; classification; prediction; student succes; higher education;
    All these keywords.

    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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