Item selection and exposure control methods for computerized adaptive testing with multidimensional ranking items

Chia‐Wen CHEN, Wen Chung WANG, Ming Ming CHIU, Sage RO

Research output: Contribution to journalArticles

Abstract

The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across items). To address these issues, we introduce subpool partition strategies for item selection and within‐person statement exposure control procedures. Simulations showed that the multinomial method reduces computation time while maintaining measurement precision. Both the freeze and revised Sympson‐Hetter online (RSHO) methods controlled the statement exposure rate; RSHO sacrificed some measurement precision but increased pool use. Furthermore, preventing a statement's repetition on consecutive items neither hindered the effectiveness of the freeze or RSHO method nor reduced measurement precision. Copyright © 2019 by the National Council on Measurement in Education.
Original languageEnglish
Pages (from-to)343-369
JournalJournal of Educational Measurement
Volume57
Issue number2
Early online date12 Sep 2019
DOIs
Publication statusPublished - 2020

Citation

Chen, C.‐W., Wang, W.‐C., Chiu, M. M., & Ro, S. (2020). Item selection and exposure control methods for computerized adaptive testing with multidimensional ranking items. Journal of Educational Measurement, 57(2), 343-369. doi: 10.1111/jedm.12252

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