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

Authors

Zhidong Zhang, The University of Texas Rio Grande Valley
Angelica Guanzon, The University of Texas Rio Grande Valley

Document Type

Article

Publication Date

10-11-2022

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—a 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.

Comments

Copyright for this article is retained by the author(s), with first publication rights granted to the journal.

Recommended Citation

Zhang, Z., & Guanzon, A. (2022). A Mixed Assessment for the Science Learning via a Bayesian Network Representation. Journal of Education and Development, 6(5), 1. https://doi.org/10.20849/jed.v6i5.1309

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Journal of Education and Development