{"title":"Line Graph Competence in Chemistry: Introducing the Receptive Graph Competence Model (RGC-Chem)","authors":"Katharina Groß, Beate Fichtner","doi":"10.1021/acs.jchemed.6c00562","DOIUrl":null,"url":null,"abstract":"<p>In chemistry, a visual science, the ability to read,\r\ninterpret,\r\nand reason with graphs is essential for conceptual understanding.\r\nHowever, existing models of graph competence have largely been developed\r\noutside chemistry and primarily focus on the formal-representational\r\ndimension of graph comprehension. As such, they do not adequately\r\ncapture the domain-specific cognitive demands that arise when learners\r\nengage with chemistry-specific graphs. This paper introduces the Receptive\r\nGraph Competence Model for Chemistry (RGC-Chem), a hierarchically\r\nstructured, domain-specific framework for the analysis, instruction,\r\nand assessment of receptive line graph competence in chemistry. The\r\nmodel comprises five hierarchically ordered levels, ranging from the\r\nidentification of individual function values (Level 1) to the integrated\r\ninterpretation of more than one graph (Level 5), and integrates two\r\ncomplementary dimensions: a formal-representational dimension addressing\r\nthe structural conventions of graphs and a domain-specific content\r\ndimension encompassing the chemistry-related conceptual knowledge\r\nrequired for comprehensive graph understanding. The RGC-Chem was developed\r\nusing a deductive–inductive approach, grounded in established\r\ntheoretical frameworks and empirically refined through a systematic\r\ntextbook analysis of 371 line-graph-related tasks from German secondary\r\nchemistry textbooks. The findings indicate that the model’s\r\ncompetence levels accommodate the full range of graph-related task\r\ndemands identified in these textbooks and support the model’s\r\npractical utility as a research and instructional tool. The paper\r\nalso discusses the model’s potential for task design, assessment,\r\nand instructional planning, as well as its current limitations, particularly\r\nthe need for empirical validation with learners. Overall, the RGC-Chem\r\nprovides a theoretically grounded framework for research and practice\r\nrelated to graph competence in chemistry education and highlights\r\nthe importance of connecting graphical patterns to their underlying\r\nchemical meaning.</p>","PeriodicalId":43,"journal":{"name":"Journal of Chemical Education","volume":"103 10","pages":"5612–5622"},"PeriodicalIF":3.0000,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Education","FirstCategoryId":"92","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jchemed.6c00562","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/10/1 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Chemical Education is the official journal of the Division of Chemical Education of the American Chemical Society, co-published with the American Chemical Society Publications Division. Launched in 1924, the Journal of Chemical Education is the world’s premier chemical education journal. The Journal publishes peer-reviewed articles and related information as a resource to those in the field of chemical education and to those institutions that serve them. JCE typically addresses chemical content, activities, laboratory experiments, instructional methods, and pedagogies. The Journal serves as a means of communication among people across the world who are interested in the teaching and learning of chemistry. This includes instructors of chemistry from middle school through graduate school, professional staff who support these teaching activities, as well as some scientists in commerce, industry, and government.