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A Framework for Measuring the Amount of Adaptation of Rasch‐based Computerized Adaptive Tests
Author(s) -
Wyse Adam E.,
McBride James R.
Publication year - 2020
Publication title -
journal of educational measurement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.917
H-Index - 47
eISSN - 1745-3984
pISSN - 0022-0655
DOI - 10.1111/jedm.12267
Subject(s) - rasch model , adaptation (eye) , computerized adaptive testing , polytomous rasch model , measure (data warehouse) , computer science , item response theory , test (biology) , item analysis , statistics , machine learning , psychometrics , psychology , artificial intelligence , data mining , mathematics , neuroscience , paleontology , biology
A key consideration when giving any computerized adaptive test (CAT) is how much adaptation is present when the test is used in practice. This study introduces a new framework to measure the amount of adaptation of Rasch‐based CATs based on looking at the differences between the selected item locations (Rasch item difficulty parameters) of the administered items and target item locations determined from provisional ability estimates at the start of each item. Several new indices based on this framework are introduced and compared to previously suggested measures of adaptation using simulated and real test data. Results from the simulation indicate that some previously suggested indices are not as sensitive to changes in item pool size and the use of constraints as the new indices and may not work as well under different item selection rules. The simulation study and real data example also illustrate the utility of using the new indices to measure adaptation at both a group and individual level. Discussion is provided on how one may use several of the indices to measure adaptation of Rasch‐based CATs in practice.