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
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24.8K
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794
Authors
415
Institutions
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2017–2026
59
Variants
Item Selection Procedures
Parents
353
The Multidimensional Assessment of Interoceptive Awareness (MAIA)
Wolf Mehling, Cynthia Price, Jennifer Daubenmier et al. · PLoS ONE · 2012 · 1.4K citations · Full text
Evaluation of Item-Based Top- <i>N</i> Recommendation Algorithms
George Karypis · 2001 · 587 citations
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University of Illinois Urbana-Champaign
Urbana, United States
17 h-index
29 publications
1.3K citations
Educational Testing Service
Princeton, United States
15 h-index
36 publications
2.2K citations

University of Twente
Enschede, The Netherlands
15 h-index
28 publications
1.7K citations

Universidad Autónoma de Madrid
Madrid, Spain
9 h-index
23 publications
774 citations
Cito (Netherlands)
Arnhem, Netherlands
7 h-index
9 publications
435 citations
The University of Texas at Austin
Austin, United States
7 h-index
10 publications
325 citations
Education University of Hong Kong
Tai Po, Hong Kong
7 h-index
8 publications
177 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
7 h-index
12 publications
1.1K citations
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63 publications · 3.7K citations
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33 publications · 1.2K citations
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23 publications · 784 citations
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14 publications · 611 citations
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8 publications · 417 citations
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