Mutual Information Item Selection Method in Cognitive Diagnostic Computerized Adaptive Testing With Short Test Length
Bibliographic Data
| ID | 20284355 |
|---|---|
| Authors | Chun Wang (0009-0002-7869-0892, University of Minnesota, Minneapolis, MN, USA, corresponding author) |
| Year | 2013 |
| Volume | 73 |
| Issue | 6 |
| Pages | 1017-1035 |
| Publication date | 2013-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/0013164413498256 |
| OpenAlex | W1999669349 |
| Language | EN |
| Citations received | 5 |
| References cited | 34 |
Cognitive diagnostic computerized adaptive testing (CD-CAT) purports to combine the strengths of both CAT and cognitive diagnosis. Cognitive diagnosis models aim at classifying examinees into the correct mastery profile group so as to pinpoint the strengths and weakness of each examinee whereas CAT algorithms choose items to determine those strengths and weakness as efficiently as possible. Most of the existing CD-CAT item selection algorithms are evaluated when test length is relatively long whereas several applications of CD-CAT, such as in interim assessment, require an item selection algorithm that is able to accurately recover examinees’ mastery profile with short test length. In this article, we introduce the mutual information item selection method in the context of CD-CAT and then provide a computationally easier formula to make the method more amenable in real time. Mutual information is then evaluated against common item selection methods, such as Kullback–Leibler information, posterior weighted Kullback–Leibler information, and Shannon entropy. Based on our simulations, mutual information consistently results in nearly the highest attribute and pattern recovery rate in more than half of the conditions. We conclude by discussing how the number of attributes, Q-matrix structure, correlations among the attributes, and item quality affect estimation accuracy
Cognition · Computerized adaptive testing · Context (archaeology) · Data mining · Entropy (arrow of time) · Item response theory · Machine learning · Mutual information · Psychometrics · Selection (genetic algorithm) · Statistics · Test (biology) · Artificial Intelligence · Cognitive Abilities and Testing · Computer Science · Mathematics · Psychology · Psychometric Methodologies and Testing
Item Selection Strategies Based on Attribute Mastery Probabilities in CD-CAT
New item selection methods in cognitive diagnostic computerized adaptive testing
Balancing exposure and efficiency
Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests
A Note on the Relationship of the Shannon Entropy Procedure and the Jensen–Shannon Divergence in Cognitive Diagnostic Computerized Adaptive Testing
Elements of Information Theory
Multidimensional Item Response Theory
Cognitive Assessment Models with Few Assumptions, and Connections with Nonparametric Item Response Theory
Rule Space
Measurement of psychological disorders using cognitive diagnosis models.
Higher-Order Latent Trait Models for Cognitive Diagnosis
A Mathematical Theory of Communication
Mutual Information Item Selection in Adaptive Classification Testing
| Unique citing works | 5 |
|---|---|
| Citations per year | 0,45 |
| Citation span | 2015 - 2026 (12) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |