Using Bayesian Networks for Cognitive Assessment of Student Understanding of Buoyancy: A Granular Hierarchy Model. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Ling Ling; Jian, Sun Xiao; Liu, Yan Lou; Xin, Tao
• Is Part Of: Applied measurement in education. Volume 36:Issue 1(2023)
• Page Start: 45
• Page End: 59
• Publication Date: 2023-01-02
• Subjects: Educational tests and measurements -- United States -- Periodicals; Educational tests and measurements -- Periodicals; Education -- Standards -- Periodicals; 371.26
• DOI: 10.1080/08957347.2023.2172014
• Languages: English
• ISSNs: 0895-7347

Abstract

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.