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Predicting Students’ Academic Performance Based on Enrolment Data

Author

Listed:
  • Alisa Bilal Zorić

    (Polytechnic Baltazar Zaprešić, Zaprešić, Croatia)

Abstract

Efficient education is key to the development and progress of modern society. Identifying factors that affect students’ academic performance is a very important step towards efficient education. With fast IT development and lower prices, universities start to collect a huge amount of data. With data mining methods and techniques, universities can use this data, analyze it and get hidden and useful information. This paper presents a model for predicting students’ academic performance based on enrolment data using one of the data mining techniques, Neural network. The enrolment data consists of demographic and economic data and information about previous education. Students’ academic performance is measured by grade point average in university, and based on that, students are divided into two groups. One group consists of students with a grade point average below 3.5, and the other group consists of students with a grade point average above 3.5. This model may represent the first step for educators to early intervene and reduce the percentage of students leaving universities. They could offer students who are classified below average some additional classes to overcome the more difficult courses because of insufficient prior knowledge, thereby, increasing their likelihood of continuing their studies.

Suggested Citation

  • 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.
    • Handle: RePEc:mgs:ijoied:v:6:y:2020:i:4:p:54-61
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    References listed on IDEAS

    as
    1. Oancea, Bogdan & Dragoescu, Raluca & Ciucu, Stefan, 2013. "Predicting students’ results in higher education using a neural network," MPRA Paper 72041, University Library of Munich, Germany.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Neural networks; Educational Data mining; Student’s academic performance;
    All these keywords.

    JEL classification:

    • M00 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General - - - General

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