ERIC Number: EJ1432640
Record Type: Journal
Publication Date: 2024-Aug
Pages: 27
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0013-1644
EISSN: EISSN-1552-3888
Available Date: N/A
Multimodal Data Fusion to Detect Preknowledge Test-Taking Behavior Using Machine Learning
Educational and Psychological Measurement, v84 n4 p753-779 2024
In various fields, including college admission, medical board certifications, and military recruitment, high-stakes decisions are frequently made based on scores obtained from large-scale assessments. These decisions necessitate precise and reliable scores that enable valid inferences to be drawn about test-takers. However, the ability of such tests to provide reliable, accurate inference on a test-taker's performance could be jeopardized by aberrant test-taking practices, for instance, practicing real items prior to the test. As a result, it is crucial for administrators of such assessments to develop strategies that detect potential aberrant test-takers after data collection. The aim of this study is to explore the implementation of machine learning methods in combination with multimodal data fusion strategies that integrate bio-information technology, such as eye-tracking, and psychometric measures, including response times and item responses, to detect aberrant test-taking behaviors in technology-assisted remote testing settings.
Descriptors: Prior Learning, Testing, Behavior, Artificial Intelligence, Data Use, Decision Making, Biofeedback, Behavior Patterns, Psychometrics, Item Response Theory, Time Perspective, Computer Assisted Testing, Cheating, Information Technology
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: R305A210428
Author Affiliations: N/A