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Comparison of Classifiers for Eye-Tracking Data

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dc.contributor.authorLandes, Jennifer
dc.contributor.authorKöppl, Sonja
dc.contributor.authorKlettke, Meike
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:13Z
dc.date.available2024-10-21T18:24:13Z
dc.date.issued2024
dc.description.abstractThis 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 metrics such as accuracy, precision, recall, F1 score, confusion matrix, and feature importance. Results indicate that Random Forest and its optimized version exhibit balanced performance, making them promising candidates for cheating prediction. The overarching research project investigates academic misconduct in the realm of online assessments, seeking to comprehend the behaviors and methodologies involved. An eye-tracking experiment was conducted to gain deeper insights into the timing and mannerisms of students engaging in academic misconduct.en
dc.identifier.doi10.18420/inf2024_126
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-746-3
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45099
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectEye Tracking
dc.subjectData Preprocessing
dc.subjectData Analysis
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectClassification
dc.subjectAcademic Cheating
dc.titleComparison of Classifiers for Eye-Tracking Dataen
dc.typeText/Conference Paper
gi.citation.endPage1462
gi.citation.publisherPlaceBonn
gi.citation.startPage1449
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleData Science Projekte: Von der Wissenschaft bis zur Anwendung

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