Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/207664 
Autor:innen: 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019 [Publisher:] IRENET - Society for Advancing Innovation and Research in Economy [Place:] Zagreb [Year:] 2019 [Pages:] 58-66
Verlag: 
IRENET - Society for Advancing Innovation and Research in Economy, Zagreb
Zusammenfassung: 
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.
Schlagwörter: 
neural networks
educational data mining
student success
JEL: 
C45
I21
Creative-Commons-Lizenz: 
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Dokumentart: 
Conference Paper

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