Influence Factors on Academic Integrity revealed by Machine Learning Methods

URN to cite this document:: urn:nbn:de:bvb:355-epub-771216
DOI to cite this document:: 10.5283/epub.77121

Landes, Jennifer

; Köppl, Sonja ; Klettke, Meike

License: Creative Commons Attribution 4.0
PDF
(1MB)

Date of publication of this fulltext: 10 Jul 2025 09:32

Alternative links to fulltext:Publisher

Details

Item type:	Conference or workshop item (Paper)
Open Access Type:	CC-License
Title of Book:	Proceedings of the 34th GI-Workshop on Foundations of Databases (Grundlagen von Datenbanken)
Publisher:	CEUR-WS.org
Other Series:	CEUR Workshop Proceedings
Volume:	3714
Date:	2023
Institutions:	Informatics and Data Science > Department Foundation of Computer Science > Data Engineering (Prof. Dr.-Ing. Meike Klettke)
Dewey Decimal Classification:	000 Computer science, information & general works > 004 Computer science
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Partially
Item ID:	77121

Preview

Export bibliographical data

Abstract

Academic integrity in higher education can be influenced by individual or by institutional factors. Cheating behavior undermines the academic integrity of the learning environment and can have negative consequences for both the individual student and the academic community. To understand the factors that influence the cheating behavior of students, a quantitative study was conducted, specifically ...