Predicting Students’ Success Using Neural Network

2019 ENTRENOVA Conference Proceedings

9 Pages Posted: 10 Dec 2019

See all articles by Alisa Bilal Zorić

Alisa Bilal Zorić

University of Applied Sciences Baltazar Zaprešić

Date Written: September 12, 2019

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’ enrollment 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

JEL Classification: C45, I21

Suggested Citation

Bilal Zorić, Alisa, Predicting Students’ Success Using Neural Network (September 12, 2019). 2019 ENTRENOVA Conference Proceedings, Available at SSRN: https://ssrn.com/abstract=3490105 or http://dx.doi.org/10.2139/ssrn.3490105

Alisa Bilal Zorić (Contact Author)

University of Applied Sciences Baltazar Zaprešić ( email )

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