基于贝叶斯网络表征的科学学习混合评估

Zhidong Zhang, Angeli B. Guanzon
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引用次数: 3

摘要

本研究探索另一种评估模式来检视化学学习者的学习进度。以“化学学习中问题解决的评价”为模型,反映了学生对化学学习的掌握程度。数据来自日记叙述,并通过认知任务分析进行分析。在此基础上,建立了以结构形式表示定性信息的学生模型,并为定量表示提供了评价模型的潜在框架——贝叶斯网络评价模型。通过贝叶斯网络评估模型对学生的成绩进行评估,并将其分为低水平、中水平和高水平三大类。陈述性知识、程序性知识和战略性知识的掌握水平至少在90.51/100分以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Mixed Assessment for the Science Learning via a Bayesian Network Representation
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.
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