Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Minimizing Examinee Collusion with a Latin- Square Treatment Structure

Authors: M. H. Omar

Abstract:

Cheating on standardized tests has been a major concern as it potentially minimizes measurement precision. One major way to reduce cheating by collusion is to administer multiple forms of a test. Even with this approach, potential collusion is still quite large. A Latin-square treatment structure for distributing multiple forms is proposed to further reduce the colluding potential. An index to measure the extent of colluding potential is also proposed. Finally, with a simple algorithm, the various Latin-squares were explored to find the best structure to keep the colluding potential to a minimum.

Keywords: Colluding pairs, Scale for Colluding Potential, Latin-Square Structure, Minimization of Cheating.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056474

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 845

References:


[1] G. J. Cizek, Cheating on Tests, How to Do It, Detect It, and Prevent It. Mahwah, New Jersey: Lawrence Erlbaum Associates, 1999.
[2] M. Golub-Smith, "A Study of the Effects of Item Option Rearrangement on the Listening Comprehension Section of the Test of English as a Foreign Language," Educational Testing Service, Princeton, New Jersey, TOEFL Res. Rep. 24 (RR-87-17), Aug 1987.
[3] A. Ercole, K. D. Whittlestone, D. G. Melvin and J. Rashbass, "Collusion detection in multiple choice examinations," Medical Education, vol. 36, no. 2, pp. 166 - 172, Feb. 2002.
[4] L. R. Nelson, "Using Selected Indices to Monitor Cheating on Multiplechoice Exams,- Journal of Educational Research and Measurement, Volume 4, 2006.
[5] S. Martirosyan, "Perfect Hash Families, Identifiable Parent Property Codes and Covering Arrays," Ph.D. dissertation, Dept. Mathematics., Universit¨at Duisburg-Essen., Armenia, 2003.
[6] G. A. Milliken and D. A. Johnson, Analysis of Messy Data, Volume I: Designed Experiments. New York: Van Nostrand Reinhold, 1992.