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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Graph Theory Approach to Detect Examinees Involved in Test Collusion.

Dmitry I Belov1, James A Wollack2

  • 1Law School Admission Council, Newtown, PA, USA.

Applied Psychological Measurement
|June 28, 2021
PubMed
Summary
This summary is machine-generated.

Test collusion (TC), the sharing of test answers, threatens score validity. A new graph theory method effectively identifies groups involved in TC by analyzing response similarities, offering a robust detection approach.

Keywords:
Markov chain Monte Carlograph theoryhypothesis testingitem preknowledgemaximum clique problemtest collusiontest security

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Area of Science:

  • Psychometrics
  • Graph Theory
  • Educational Measurement

Background:

  • Test collusion (TC) involves sharing test materials or answers, posing a significant threat to the validity of educational assessments.
  • Item preknowledge is a critical form of TC, potentially granting unfair advantages to examinees.

Purpose of the Study:

  • To introduce a novel graph theory-based methodology for detecting test collusion.
  • To identify groups of examinees engaged in TC without prior knowledge of affected test sections.

Main Methods:

  • Application of graph theory to analyze response similarity among examinees.
  • Utilizing various response similarity indices tailored to specific TC types.
  • Identifying groups through connected components, cliques, or near-cliques.

Main Results:

  • The proposed graph theory approach effectively identifies groups involved in test collusion.
  • Demonstrated effectiveness through comparisons with existing methods using real and simulated data.

Conclusions:

  • Graph theory offers a powerful framework for detecting test collusion.
  • The method provides a flexible and robust tool for enhancing test security and score validity.