Predicting Students’ Success Using Neural Networks

Alisa Bilal Zorić

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, 2019, pp 58-66 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

Abstract: Fast technological changes and constant growth of knowledge in many areas have led to an increasing importance of different approach to education. Efficient education is the foundation of modern society and it has the most important role in preparing students for a very flexible labour market. Education is key for development and progress. The goal of this paper is to present a model for predicting students’ success using Neural networks. The model is based on students’ enrolment data that consisted of demographic and economic data and information about previous education. Students’ efficacy is measured by grade point average in college, and students are divided into two groups: with grade point average below and above 3.5. This model can help educators to prepare students who are classified below average with additional classes to overcome the more difficult courses and, thus, reduce the percentage of students leaving the college because of insufficient prior knowledge.

Keywords: neural networks; educational data mining; student success (search for similar items in EconPapers)
JEL-codes: C45 I21 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:entr19:207664

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