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Journal of Chemical Education· 2026Q2

Line Graph Competence in Chemistry: Introducing the Receptive Graph Competence Model (RGC-Chem)

Katharina Groß, Beate Fichtner

Short summary

The Receptive Graph Competence Model for Chemistry (RGC-Chem) is introduced, a domain-specific framework with five hierarchical levels to analyze, instruct, and assess students' ability to interpret chemistry line graphs.

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Key points

  • Introduces the Receptive Graph Competence Model for Chemistry (RGC-Chem), a domain-specific framework for line graph interpretation.
  • The model has five hierarchical levels, progressing from basic data identification to complex multi-graph interpretation.
  • RGC-Chem integrates formal-representational and domain-specific chemistry content dimensions.
  • Developed deductively and inductively, refined by analyzing 371 chemistry textbook graph tasks.
  • The model accommodates the range of graph task demands found in secondary chemistry textbooks.

AI-generated from the title and abstract; the full text is not read.

Abstract

Abstract In chemistry, a visual science, the ability to read, interpret, and reason with graphs is essential for conceptual understanding. However, existing models of graph competence have largely been developed outside chemistry and primarily focus on the formal-representational dimension of graph comprehension. As such, they do not adequately capture the domain-specific cognitive demands that arise when learners engage with chemistry-specific graphs. This paper introduces the Receptive Graph Competence Model for Chemistry (RGC-Chem), a hierarchically structured, domain-specific framework for the analysis, instruction, and assessment of receptive line graph competence in chemistry. The model comprises five hierarchically ordered levels, ranging from the identification of individual function values (Level 1) to the integrated interpretation of more than one graph (Level 5), and integrates two complementary dimensions: a formal-representational dimension addressing the structural conventions of graphs and a domain-specific content dimension encompassing the chemistry-related conceptual knowledge required for comprehensive graph understanding. The RGC-Chem was developed using a deductive–inductive approach, grounded in established theoretical frameworks and empirically refined through a systematic textbook analysis of 371 line-graph-related tasks from German secondary chemistry textbooks. The findings indicate that the model’s competence levels accommodate the full range of graph-related task demands identified in these textbooks and support the model’s practical utility as a research and instructional tool. The paper also discusses the model’s potential for task design, assessment, and instructional planning, as well as its current limitations, particularly the need for empirical validation with learners. Overall, the RGC-Chem provides a theoretically grounded framework for research and practice related to graph competence in chemistry education and highlights the importance of connecting graphical patterns to their underlying chemical meaning.

The authors' abstract, as published at the source. Journal of Chemical Education, 2026 · DOI ↗

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Field: Education

EducationSocial Sciences