Assessing concept mapping competence using item expansion-based diagnostic classification analysis

IF 3.6 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Journal of Research in Science Teaching Pub Date : 2023-08-29 DOI:10.1002/tea.21897
Shulan Xia, Peida Zhan, Kennedy Kam Ho Chan, Lijun Wang
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Abstract

Concept mapping is widely used as a tool for assessing students' understanding of science. To fully realize the diagnostic potential of concept mapping, a scoring method that not only provides an objective and accurate assessment of students' drawn concept maps but also provides a detailed understanding of students' proficiency and deficiencies in knowledge is necessary. However, few of the existing scoring methods focus on the latent constructs (e.g., knowledge, skills, and cognitive processes) that guide the creation of concept maps. Instead, they focus on the completeness of the concept map by assigning a composite score, which makes it difficult to generate targeted diagnostic feedback information for advancing students' learning. To apply the diagnostic classification model to the quantitative analysis of concept maps, this study introduced the novel application of the item expansion-based diagnostic classification analysis (IE-DCA) for this purpose. The IE-DCA can not only assess students' concept mapping abilities along a continuum but also classify students according to their concept mapping attributes when constructing the concept maps. The application and benefits of this approach were illustrated using a physics concept-mapping item related to particle and rigid body. Results showed that the estimated attribute profiles via the IE-DCA provided more detailed information about students' latent constructs than the composite score. Overall, this study illustrates the feasibility and potential of applying IE-DCA to analyze concept maps. Future applications of IE-DCS in other assessments in science education are discussed.

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使用基于项目扩展的诊断分类分析评估概念图能力
概念图被广泛用作评估学生对科学理解的工具。为了充分发挥概念图的诊断潜力,有必要采用一种评分方法,既能客观准确地评估学生绘制的概念图,又能详细了解学生的熟练程度和知识不足。然而,现有的评分方法很少关注指导概念图创建的潜在结构(如知识、技能和认知过程)。相反,他们通过分配综合分数来关注概念图的完整性,这使得很难生成有针对性的诊断反馈信息来促进学生的学习。为了将诊断分类模型应用于概念图的定量分析,本研究引入了基于项目扩展的诊断分类分析(IE‐DCA)的新应用。IE‐DCA不仅可以评估学生在连续体上的概念映射能力,而且在构建概念图时可以根据学生的概念映射属性对学生进行分类。使用与粒子和刚体相关的物理概念映射项目说明了该方法的应用和优点。结果表明,通过IE‐DCA估计的属性档案比综合得分提供了更多关于学生潜在结构的详细信息。总体而言,本研究说明了应用IE‐DCA分析概念图的可行性和潜力。讨论了IE‐DCS在科学教育其他评估中的未来应用。
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来源期刊
Journal of Research in Science Teaching
Journal of Research in Science Teaching EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
8.80
自引率
19.60%
发文量
96
期刊介绍: Journal of Research in Science Teaching, the official journal of NARST: A Worldwide Organization for Improving Science Teaching and Learning Through Research, publishes reports for science education researchers and practitioners on issues of science teaching and learning and science education policy. Scholarly manuscripts within the domain of the Journal of Research in Science Teaching include, but are not limited to, investigations employing qualitative, ethnographic, historical, survey, philosophical, case study research, quantitative, experimental, quasi-experimental, data mining, and data analytics approaches; position papers; policy perspectives; critical reviews of the literature; and comments and criticism.
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