Item Selection Algorithms

Item Selection Algorithms is a computational method in adaptive testing that determines the optimal sequence of assessment items by selecting those that maximize information about an examinee’s ability level at each stage of the test, based on prior responses and psychometric models.

353

Publications

24.8K

Citations

794

Authors

415

Institutions

Publications per year

2017–2026

59

Variants

Item Selection Procedures

Authors

794

  1. WJ
    Wim J. van der Linden

    University of Twente, Netherlands

    14 publications · 1K citations

    13

    h-index

  2. HC
    Hua-Hua Chang

    University of Illinois Urbana-Champaign, United States

    12 publications · 1.2K citations

    12

    h-index

  3. HC
    Hua‐Hua Chang

    University of Illinois Urbana-Champaign, United States

    11 publications · 419 citations

    11

    h-index

  4. CW
    Chun Wang

    University of Minnesota, United States

    8 publications · 393 citations

    8

    h-index

  5. TJ
    Theo J. H. M. Eggen

    University of Twente, Netherlands

    7 publications · 336 citations

    7

    h-index

1–5 of 794

Venues

  1. Applied Psychological Measurement

    63 publications · 3.7K citations

    33

    h-index

  2. Educational and Psychological Measurement

    33 publications · 1.2K citations

    22

    h-index

  3. Journal of Educational Measurement

    23 publications · 784 citations

    17

    h-index

  4. Psychometrika

    14 publications · 611 citations

    12

    h-index

  5. 8

    h-index

1–5