A Mixed Assessment for the Science Learning via a Bayesian Network Representation

Zhidong Zhang and Angelica Guanzon
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Zhidong Zhang: The University of Texas-Rio Grande Valley
Angelica Guanzon: The University of Texas-Rio Grande Valley

Journal of Education and Development, 2022, vol. 6, issue 5, 1-7

Abstract: This study explored an alternative assessment model to examine Chemistry learners¡¯ progress. ¡°The Assessment of Problem-Solving in Chemistry Learning¡± as a model represented students¡¯ mastery of chemistry study. The data were from journaling narratives and analyzed through cognitive task analysis. Based on the analyses, a student model was established, which represents the qualitative information in a structure, and provides a potential framework of the assessment model for the quantitative representation¡ªa Bayesian network assessment model. The student¡¯s performance was assessed via the Bayesian network assessment model, and classified into three categories: low level, middle level, and high level. The mastery level should be at least scored at and above 90.51/100 for Declarative, Procedural, and Strategic Knowledge respectively.

Keywords: science learning; Bayesian network representation; student modeling; diagnostically cognitive assessment; and mixed methods design (search for similar items in EconPapers)
JEL-codes: I21 I24 J13 Z13 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:cxp:jededu:v:6:y:2022:i:5:p:1-7

DOI: 10.20849/jed.v6i5.1309

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