A Discrete Multiobjective Particle Swarm Optimizer for Automated Assembly of Parallel Cognitive Diagnosis Tests

Parallel test assembly has long been an important yet challenging topic in educational assessment. Cognitive diagnosis models (CDMs) are a new class of assessment models and have drawn increasing attention for being able to measure examinees' ability in detail. However, few studies have been de...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 49; H. 7; S. 2792 - 2805
Hauptverfasser: Lin, Ying, Jiang, Ye-Shi, Gong, Yue-Jiao, Zhan, Zhi-Hui, Zhang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Assembly
Cognitive diagnosis models (CDMs)
Combinatorial analysis
Cybernetics
Diagnosis
Diagnostic systems
Heuristic algorithms
Indexes
multiobjective
Multiple objective analysis
Optimization
parallel test assembly
Particle swarm optimization
particle swarm optimizer (PSO)
Reliability
Search problems
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:Parallel test assembly has long been an important yet challenging topic in educational assessment. Cognitive diagnosis models (CDMs) are a new class of assessment models and have drawn increasing attention for being able to measure examinees' ability in detail. However, few studies have been devoted to the parallel test assembly problem in CDMs (CDM-PTA). To fill the gap, this paper models CDM-PTA as a subset-based bi-objective combinatorial optimization problem. Given an item bank, it aims to find a required number of tests that achieve optimal but balanced diagnostic performance, while satisfying important practical requests in the aspects of test length, item type distribution, and overlapping proportion. A set-based multiobjective particle swarm optimizer based on decomposition (S-MOPSO/D) is proposed to solve the problem. To coordinate with the property of CDM-PTA, S-MOPSO/D utilizes an assignment-based representation scheme and a constructive learning strategy. Through this, promising solutions can be built efficiently based on useful assignment patterns learned from personal and collective search experience on neighboring scalar problems. A heuristic constraint handling strategy is also developed to further enhance the search efficiency. Experimental results in comparison with three representative approaches validate that the proposed algorithm is effective and efficient.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2018.2836388