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ERIC Number: EJ1121544
Record Type: Journal
Publication Date: 2016
Pages: 12
Abstractor: As Provided
ISSN: ISSN-1913-9020
Assessment of Matrix Multiplication Learning with a Rule-Based Analytical Model--"A Bayesian Network Representation"
Zhang, Zhidong
International Education Studies, v9 n12 p182-193 2016
This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model, comprising of 2 layers of explanatory variables-Matrix Multiplication, Performance and Semantic Explanations; and one layer of evidential variables containing 9 evidential variables-was developed. With the simulating data, 9 students' Performance and Semantic Explanation evidences were recorded. The results indicated that the hierarchical Bayesian assessment effectively traced and recorded students' learning trajectories; and assessed students' learning dynamically and diagnostically.
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Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A