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

Author

Listed:
  • Zhidong Zhang

    (The University of Texas-Rio Grande Valley)

  • Angelica Guanzon

    (The University of Texas-Rio Grande Valley)

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.

Suggested Citation

  • Zhidong Zhang & Angelica Guanzon, 2022. "A Mixed Assessment for the Science Learning via a Bayesian Network Representation," Journal of Education and Development, Julypress, vol. 6(5), pages 1-7, November.
    • Handle: RePEc:cxp:jededu:v:6:y:2022:i:5:p:1-7
    DOI: 10.20849/jed.v6i5.1309
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    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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