Man Kaiwen
The University of Alabama, Tuscaloosa, USA.
Educ Psychol Meas. 2024 Aug;84(4):753-779. doi: 10.1177/00131644231193625. Epub 2023 Sep 19.
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.
在包括大学录取、医学委员会认证和军事征兵等各个领域，高风险决策常常基于大规模评估所得的分数做出。这些决策需要精确且可靠的分数，以便能够对考生做出有效的推断。然而，此类测试对考生表现提供可靠、准确推断的能力可能会因异常的应试行为而受到损害，例如在考试前练习真题。因此，对于此类评估的管理者而言，制定在数据收集后检测潜在异常考生的策略至关重要。本研究的目的是探索机器学习方法与多模态数据融合策略相结合的实施方式，该策略整合生物信息技术（如眼动追踪）和心理测量指标（包括反应时间和题目作答情况），以检测技术辅助远程测试环境中的异常应试行为。
