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Research on the Cognitive Diagnosis of Chinese Listening Comprehension Ability Based on the G-DINA Model

As a new generation of measurement theory, cognitive diagnosis theory shows significant potential and advantages in educational evaluation in that it combines a cognitive process and a measurement method. The application of the theory not only reveals the potential characteristics of learners in cog...

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Detalles Bibliográficos
Autores principales: Li, Li, An, Yi, Ren, Jie, Wei, Xiaoman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452943/
https://www.ncbi.nlm.nih.gov/pubmed/34557134
http://dx.doi.org/10.3389/fpsyg.2021.714568
Descripción
Sumario:As a new generation of measurement theory, cognitive diagnosis theory shows significant potential and advantages in educational evaluation in that it combines a cognitive process and a measurement method. The application of the theory not only reveals the potential characteristics of learners in cognitive processing, but also provides targeted remedies and strategic guidance for individuals. Given the difficulties of traditional assessment models in providing an insightful and fine-grained account for individualized and procedural learning, providing personalized learning strategies for learners of Chinese as a second language has been a new goal of teaching and measurement in Chinese listening. This study constructs a cognitive diagnosis model of Chinese listening comprehension for Chinese-as-a-second-language learners through theoretical exploration, model hypotheses, repeated verification, and model modification. The results show that the Q-matrix (Q(3)) constructed by the experts within modification has the highest fitting degree with the empirical data. The parameter recovery rate, the accuracy of the tested attribute or mode, and the relative fitting index obtained from the simulation study are consistent with the information extracted from the empirical data. The diagnostic reliability and effectiveness of generalized deterministic inputs, noise “and” gate (G-DINA) are higher than those of DINA, deterministic inputs, noisy “or” gate (DINO), and reduced reparametrized unified model (RRUM). In the estimation of the item and subject parameters, the G-DINA model shows good convergence, and the average classification accuracy rate based on attribute level is 0.861.