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Few and Different: Detecting Examinees With Preknowledge Using Extended Isolation Forests.

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Summary
This summary is machine-generated.

This study introduces a new method to detect examinees with prior knowledge of test items, even when the compromised items are unknown. The extended isolation forest algorithm effectively identifies these examinees using response time and accuracy data.

Keywords:
isolated forestsitem compromisepreknowledgetest security

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Machine Learning in Education

Background:

  • Item preknowledge compromises test score validity and impacts item parameter estimation.
  • Identifying examinees with prior knowledge (EWPs) and compromised items (CIs) is crucial for high-stakes testing.
  • Existing methods often require knowledge of compromised items, which is not always available.

Purpose of the Study:

  • To develop a method for detecting examinees with prior knowledge (EWPs) without prior knowledge of compromised items (CIs).
  • To investigate the effectiveness of unsupervised machine learning for identifying EWPs based on response behavior.
  • To analyze response time (RT) and response accuracy (RA) as indicators of item preknowledge.

Main Methods:

  • Utilized an unsupervised machine learning algorithm, extended isolation forest (EIF).
  • Analyzed examinee response behavior, specifically response time (RT) and response accuracy (RA).
  • Focused on detecting EWPs in scenarios where compromised items (CIs) are unknown.

Main Results:

  • The extended isolation forest (EIF) algorithm demonstrated utility in detecting examinees with prior knowledge (EWPs).
  • Response behavior, including response time (RT) and response accuracy (RA), served as effective indicators.
  • The method allows for the identification of EWPs even when compromised items are not pre-identified.

Conclusions:

  • Unsupervised machine learning, specifically EIF, offers a viable approach for identifying examinees with prior knowledge (EWPs).
  • This method enhances the validity of test scores by detecting data contamination.
  • Routine monitoring for EWPs is essential for maintaining the integrity of high-stakes testing programs.