Journal of Computerized Adaptive Testing

About the Journal

JCAT is a peer-reviewed electronic journal designed to advance the science and practice of computerized adaptive testing (CAT). JCAT publishes two types of manuscripts:

  1. Empirical research reports, theoretical papers, and integrative critical reviews on topics directly related to CAT (e.g., item selection algorithms, security algorithms, multistage designs, examinee reactions to CAT, DIF in CAT, item bank development, the psychometrics of CAT) and on important ancillary topics (e.g., innovative item types, assessment engineering, psychometric models, issues surrounding the technology of adaptive testing, validity studies).
  2. Applications and implementations of CAT. These articles include descriptions of specific decisions made for a particular purpose, required by the nature of the adaptive test being developed, including (but not limited to) the nature of the testing population, the type of decisions being made with the information from the test, the size of the available item bank, the changing nature of item styles, approaches to field testing, and complex item selection procedures.

JCAT is the official journal of the International Association for Computerized Adaptive Testing: IACAT.ORG

To submit a manuscript, select the green "Information -- For Authors" link on the bottom right, and follow the instructions.

To subscribe to JCAT, select the "Information -- For Readers" link on the bottom right.  Subscriptions are free.

To access articles published in the current year, select CURRENT on the top line of any page.  To access articles from previous years, select ARCHIVES.

 

Editor

Dr. Duanli Yan, Director of Data Analysis and Computational Research , Educational Testing Service, U.S.A.

Production Editor

Matthew D, Finkelman, Tufts University

Consulting Editors

  • John Barnard, EPEC, Australia
  • Kirk A. Becker, Pearson VUE, United States
  • Matthew D. Finkelman, Tufts University School of Dental Medicine, United States
  • Andreas Frey, Friedrich Schiller University Jena, Germany
  • Kyung T. Han, Graduate Management Admission Council, United States
  • G. Gage Kingsbury, Psychometric Consultant, United States
  • Alan D Mead, Talent Algorithms Inc., United States
  • Mark D Reckase, Michigan State University, United States
  • Daniel O. Segall, PMC, United States
  • Bernard P Veldkamp, University of Twente, Netherlands
  • Wim van der Linden
  • Alina von Davier, Duolingo
  • Steven L. Wise, Northwest Evaluation Association, United States

Announcements

Current Issue

Vol. 13 No. 2 (2026): Item Selection Rules for Content Adaptive Progress Testing

Content adaptive progress testing (CAPT) introduces a new approach to longitudinal testing of candidates who take progress tests as part of their educational program. CAPT assessments adapt between subsequent assessments, tailoring items on an individual level to assist each candidate in demonstrating knowledge across many topics which are all mapped to the overall course learning outcomes. In this way, success is measured by topic completion on an individual level. Some of the key benefits include a truly personalized learning experience, with individual feedback throughout, while also tracking each candidate’s progress against a single long-term goal of demonstrating attainment across all required topics. For the learning experience to be personalized, there needs to be automatic software selection of different items for each candidate based on their performance across a sequence of assessments. The advantages, disadvantages and practical implementation of different item selection rules are discussed. There is a need to ensure that no item is repeated to the same candidate in any test: No two items in the same test belong to the same topic for the same candidate, a minimum number of topics per broader area are administered in every assessment, and selection of incomplete topics is prioritized over complete topics. These rules need to be clear and transparent to motivate appropriate learning.

Published: 2026-06-05

Articles

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