Impact of Preprocessing on Classification Results of Eye-Tracking-Data

URN to cite this document:: urn:nbn:de:bvb:355-epub-781994
DOI to cite this document:: 10.5283/epub.78199

Landes, Jennifer

; Klettke, Meike

; Köppl, Sonja

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 25 Nov 2025 05:50

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Abstract

Eye-Tracking data provides valuable insights into human behavior, yet its high variability to noise require robust preprocessing to ensure meaningful analysis. This study introduces and evaluates a systematic preprocessing pipeline tailored to enhance machine learning classifier performance in the context of Eye-Tracking data, on a dataset on academic cheating detection. Unlike prior work ...