Comparison of Classifiers for Eye-Tracking Data

URN to cite this document:: urn:nbn:de:bvb:355-epub-770829
DOI to cite this document:: 10.5283/epub.77082

Landes, Jennifer

; Köppl, Sonja ; Klettke, Meike

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 10 Jul 2025 09:23

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Item type:	Conference or workshop item (Paper)
ISBN:	3-88579-746-1, 978-3-88579-746-3
Open Access Type:	CC-License
Title of Book:	Informatik 2024 : Lock-in or log out? Wie digitale Souveränität gelingt
Publisher:	Gesellschaft für Informatik
Place of Publication:	Bonn
Other Series:	Lecture notes in Informatics (LNI)
Volume:	P-352
Page Range:	pp. 1449-1462
Date:	2024
Institutions:	Informatics and Data Science > Department Foundation of Computer Science > Data Engineering (Prof. Dr.-Ing. Meike Klettke)
Keywords:	Eye Tracking, Data Preprocessing, Data Analysis, Machine Learning, Random Forest, Classification, Academic Cheating
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:	77082

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Abstract

This paper delves into the initial stages of data analysis, focusing on the classification of eye-tracking data. Six machine learning algorithms, namely XGBoost, Random Forest, Naive Bayes, Logistic Regression, Gradient Boosting Machines, and Neural Networks, were employed to predict cheating behavior based on a dataset comprising records from 25 students. Their performance was evaluated using ...