Using Bayesian Networks for Cognitive Assessment of Student Understanding of Buoyancy: A Granular Hierarchy Model

Autor/inn/en	Wang, Ling Ling; Jian, Sun Xiao; Liu, Yan Lou; Xin, Tao
Titel	Using Bayesian Networks for Cognitive Assessment of Student Understanding of Buoyancy: A Granular Hierarchy Model
Quelle	In: Applied Measurement in Education, 36 (2023) 1, S.45-59 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Ling Ling) ORCID (Liu, Yan Lou) ORCID (Xin, Tao)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2023.2172014
Schlagwörter	Bayesian Statistics; Networks; Cognitive Measurement; Diagnostic Tests; Student Evaluation; Knowledge Level; Scientific Concepts; Physics; Matrices; Q Methodology; Prediction + Suchen Sie Ihr Suchwort? Diagnostic test; Diagnostischer Test; Schulnote; Studentische Bewertung; Wissensbasis; Physik; Matrizenrechnung; Vorhersage
Abstract	Cognitive diagnostic assessment based on Bayesian networks (BN) is developed in this paper to evaluate student understanding of the physical concept of buoyancy. we propose a three-order granular-hierarchy BN model which accounts for both fine-grained attributes and high-level proficiencies. Conditional independence in the BN structure is tested and utilized to validate the proposed model. The proficiency relationships are verified and the initial Q-matrix is refined. Then, an optimized granular hierarchy model is constructed based on the updated Q-matrix. All variants of the constructed models are evaluated on the basis of the prediction accuracy and the goodness-of-fit test. The experimental results demonstrate that the optimized granular-hierarchy model has the best prediction and model-fitting performance. In general, the BN method not only can provide more flexible modeling approach, but also can help validate or refine the proficiency model and the Q-matrix and this method has its unique advantage in cognitive diagnosis. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01