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A concept map or conceptual diagram is a diagram that depicts suggested relationships between concepts. Concept maps may be used by instructional designers, engineers, technical writers, and others to organize and structure knowledge. A concept map typically represents ideas and information as boxes or circles, which it connects with labeled arrows, often in a downward-branching hierarchical structure but also in free-form maps. The relationship between concepts can be articulated in linking phrases such as "causes", "requires", "such as" or "contributes to". The technique for visualizing these relationships among different concepts is called concept mapping. Concept maps have been used to define the ontology of computer systems, for example with the object-role modeling or Unified Modeling Language formalism. Differences from other visualizations

Topic maps: Both concept maps and topic maps are kinds of knowledge graph

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Making sense of Knowledge Integration Maps

Beat Adrian Schwendimann

Digital knowledge maps are rich sources of information to track students’ learning. However, making sense of concept maps has been found challenging. Using multiple quantitative and qualitative methods in combination allows triangulating of changes in students’ understanding. This chapter introduces a novel form of concept map, called knowledge integration map (KIM), and uses KIMs as examples for an overview of concept map analysis methods. KIMs are a form of digital knowledge maps. KIMs have been implemented in high school science classrooms to facilitate and assess complex science topics, such as evolution. KIM analysis aims to triangulate changes in learners’ conceptual understanding through a multi-level analysis strategy, combining quantitative and qualitative methodologies. Quantitative analysis included overall, selected, and weighted propositional analysis using a knowledge integration rubric and network analysis describing changes in network density and prominence of selected concepts. Research suggests that scoring only selected propositions can be more sensitive to measuring conceptual change because it focuses on key concepts of the map. Qualitative analysis of KIMs included topographical analysis methods to describe the overall geometric structure of the map and qualitative analysis of link types. This chapter suggests that a combination of quantitative and qualitative analysis methods can capture different aspects of KIMs and can provide a rich description of changes in students’ understanding of complex topics.

Springer2014

Critiquing as an Alternative to Generating Concept Maps to Support Knowledge Integration Processes

Beat Adrian Schwendimann

As constructing concept maps from scratch can be time consuming, this study explores critiquing given concept maps with deliberate errors as an alternative. A form of concept map that distinguishes between different levels, called Knowledge Integration Map (KIM), was used as an assessment and embedded learning tool. The technology-enhanced biology unit was implemented in four high school science classes (n = 93). Student dyads in each class were randomly assigned to the KIM generation (n = 41) or critique (n = 52) task. Dyads in the generation group created their own connections from a given list of concepts, while dyads in the critique group received a concept map that included commonly found errors. KIMs in both groups consisted of the same concepts. Findings indicate that generating or critiquing KIMs can facilitate the construction of cross-level connections. Furthermore, results suggest that critiquing concept maps might be a more time-efficient alternative to generating concept maps from scratch.

Springer Int Publishing Ag2016

Comparing Expert and Novice Concept Map Construction Through a Talk-Aloud Protocol

Beat Adrian Schwendimann

Concept map analysis usually focuses only on the final product. This case study used a talk aloud protocol to study the concept map construction processes of novices and experts. Three biology experts and three novices (9th/10th grade high school students) constructed a concept map from a given list of concepts. Findings suggest that final concept maps of high performing students cannot be distinguished from expert-generated maps. However, analysis of oral elaborations during the construction process revealed that experts often used the same link labels as novices but associated more complex knowledge with the label. Some final propositions would be considered incorrect without an oral explanation. Findings suggest extending concept map evaluation by complementing the final product with an analysis of intermediate stages and accompanying elaborations. Additionally, this study highlights that each expert created a different map and that there is no single best expert map.

Springer Int Publishing Ag2016

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