Knowledge Integration Maps

Figure 1: Knowledge Integration Map (KIM)

A '''knowledge integration map''' (KIM) is a discipline-specific form of or digital knowledge map (1).Concept maps are a form of node-link diagram for organizing and representing connections between ideas as a semantic network (2). KIMs consist of concepts and labeled arrows. Different from traditional concept maps, KIMs divide the drawing area into discipline-specific areas, for example in biology into genotype/phenotype (see figure 1).

Overview

Knowledge integration maps have originally been developed at the University of California, Berkeley by Beat A. Schwendimann in the research group of Marcia Linn. KIMs can be used as learning and assessment tools in a wide variety of contexts (3). Knowledge Integration Maps cannot only be used as cognitive tools that help eliciting [[concept]]s, but also as social artifacts through which students communicate. When KIMs are [[collaboration|collaboratively]] constructed, they become shared social artifacts that can make existing and missing connections explicit and can spur discussion among students and teachers. As each connection between two concepts can consist of only one link, students need to negotiate which connection to make. This constraint requires student dyads to negotiate and make decisions about which connection to revise or add, which creates an authentic need for effective criteria and supporting evidence to distinguish among ideas in students’ repertoires. The KIM becomes a social support for prompting students to articulate their understanding and integrate their knowledge through reflection. This social process of reaching agreement is critical in shaping and sharing reflections of connections between ideas.

KIMs are based on the principles of knowledge integration (4). The knowledge integration framework suggests that scaffolding students to better integrate their ideas will support conceptual changes in students’ understanding. Knowledge integration maps can support the knowledge integration processes of eliciting a repertoire of ideas, adding ideas to the repertoire, sorting out the various connections among the ideas, and developing criteria for connections between ideas.

Characteristics

KIMs can be distinguished from other forms of concept maps by the following characteristics:

* Discipline-specific levels: This characteristic combines aspects of concept mapping with aspects of Venn diagrams. The concept map drawing area is divided into several discipline-specific vertical levels, for example genotype and phenotype (figure 2: #2). This arrangement requires learners to a) generate criteria and categorize ideas, b) sort out ideas into according levels (clustering), and c) generate connections between ideas within and across levels. Sorting out and grouping ideas spatially according to semantic similarity requires learners to generate criteria and make decisions about information structure that is latent in texts (5) (figure 2: #3: Step-by-step instructions). This is expected to support knowledge integration by showing ideas in contexts to other ideas and eliciting existing (and missing) connections within and across levels. Cross-links are especially desirable as they can be interpreted as “creative leaps on the part of the knowledge producer” (6) and support reasoning across ontologically different levels (7).

* Given list of ideas but free labels and links: Research suggests that constructing a map using a given list of ideas (forced choice design) reflected individual student differences in connected understanding better than more constrained fill-the-map forms. Many science concepts consists of a large number of ideas that often make it challenging for novices to identify key ideas (8). Providing students with a list of expert-selected key ideas can serve as signposts and model expert understanding. Concept maps generated from the same set of ideas allow for better scoring and comparison. Students’ alternatives ideas are captured in the idea placement, link labels, and link direction (figure 2: #4)

*KIM training activity: Students need initial training activities to learn the concept mapping method and generate criteria for concept map critique.

* KIM starter map: Building a concept map from scratch can be challenging (figure 2: #5). Providing a starter map could facilitate a worked-example effect.

* KIM focus question: The content-specific focus question guides the construction of the KIM as learners select ideas and generate links to answer the focus question (figure 2: #1).

* KIM feedback and revision: Feedback and revision supports students’ knowledge integration through revisiting, reflecting, and revising existing and new ideas. KIMs often need several revisions to adequately answer the focus question.

Figure 2: Knowledge Integration Map (KIM) activity sheet

Uses

Knowledge integration maps were originally designed to support inquiry-based learning in science education but their design can be adapted to any content. Initial implementation and evaluation of KIMs was conducted in K–12 science classrooms using the web-based inquiry science environment (WISE) (9). KIM can be used as planning tools, note-taking tools, learning tools, or assessment tools.

Creating an activity

1. Generate a focus question.
2. Based on field-experts and textbooks, identify key ideas for the map that allow answering the focus question adequately.
3. Structure KIM drawing area into field-specific levels, for example in biology: genotype/phenotype, or individual/population; in chemistry: micro/macro/symbolic.
4. Create a starter map.
5. Create a KIM training activity.{{citation needed|date=April 2015}}

Limitations of KIMs

Like every tool, KIMs have their strengths and limitations.

* Similar to geographical maps and [[concept map]]s, KIMs do not aim to include all but only a selection of concepts. KIMs are not as exact representations of a person’s [[mental model|cognitive structure]], but as a constraint and partial model thereof.

* KIMs constrain connections between two concepts to a single relationship, which require distinguishing and selecting between multiple possible relationships.

* Users need to learn the procedure for how to generate, interpret and revise KIMs. KIMs can only be effective after an adequate training phase.

* Generating and revising KIMs, especially less constrained forms or very large maps, can be time-intensive.

* Especially less constrained KIMs can include many different kinds of concepts and connections. The amorphousness and arbitrariness of structure, mixture of different kinds of concepts (for example: physical object, process, abstract construct, property, etc.) and different types of links (for example causal, correlational, temporal, part-whole, functional, teleological, mechanical, probabilistic, spatial, etc.) can make interpretation and evaluation challenging.

* Interpreting KIM propositions can be difficult as experts and novices might use the same expressions but with different meaning.

* KIMs focus on declarative knowledge, while other node-link diagram forms, for example flowcharts, focus more on procedural knowledge.

References

(1) Digital Knowledge Maps in Education. (2014). Digital knowledge maps in education. New York: Springer. doi:10.1007/978-1-4614-3178

(2) Schwendimann, B. A. (2015). Concept maps as versatile tools to integrate complex ideas: From kindergarten to higher and professional education. Knowledge Management & E-Learning: Special Issue on Novakian Concept Mapping in University and Professional Education, 7(1), 73-99. http://www.kmel-journal.org/ojs/index.php/online-publication/article/view/411

(3) Mapping biological ideas: Concept maps as knowledge integration tools for evolution education. 2011. Publisher=ProQuest. url=http://gradworks.umi.com/34/99/3499067.html

(4) Linn, M. C. (2006) The Knowledge Integration Perspective on Learning and Instruction. R. Sawyer (Ed.). In The Cambridge Handbook of the Learning Sciences. Cambridge, MA. Cambridge University Press

(5) Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76, 413-448.

(6) Novak, J. D., & Canas, A. J. (2006). The theory underlying concept maps and how to construct them. IHMC. http://www.ssu.ac.ir/fileadmin/templates/fa/Moavenatha/Moavenate-Amozeshi/edicupload/olymp-3.pdf

(7) Duncan, R. G., & Reiser, B. J. (2007). Reasoning across ontologically distinct levels: Students' understandings of molecular genetics. Journal of Research in Science Teaching, 44(7), 938-959. http://onlinelibrary.wiley.com/doi/10.1002/tea.20186/abstractRuiz

(8) Primo, M. A. (2000). On the use of concept maps as an assessment tool in science: What we have learned so far. Revista Electrónica De Investigación Educativa, 2(1). http://dialnet.unirioja.es/servlet/articulo?codigo=244017

(9) Schwendimann, B. A., & Linn, M. C. (2015). Comparing two forms of concept map critique activities to facilitate knowledge integration processes in evolution education. Journal of Research in Science Teaching, 4. doi:DOI 10.1002/tea.21244