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Making Sense of Knowledge Integration Maps

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Digital Knowledge Maps in Education

Abstract

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.

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References

  • Acton, W. H., Johnson, P. J., & Goldsmith, T. E. (1994). Structural knowledge assessment—comparison of referent structures. Journal of Educational Psychology, 86(2), 303–311.

    Article  Google Scholar 

  • Ainsworth, S. E. (1999). A functional taxonomy of multiple representations. Computers and Education, 33(2/3), 131–152.

    Article  Google Scholar 

  • Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16(3), 183–198.

    Article  Google Scholar 

  • Anderson, R. C. (1984). Some reflections on the acquisition of knowledge. Educational Researcher, 13(9), 5–10.

    Article  Google Scholar 

  • Ariew, A. (2003). Ernst Mayr’s ‘ultimate/proximate’ distinction reconsidered and reconstructed. Biology and Philosophy, 18(4), 553–565.

    Article  Google Scholar 

  • Austin, L. B., & Shore, B. M. (1995). Using concept mapping for assessment in physics. Physics Education, 30, 41.

    Article  Google Scholar 

  • Ausubel, D. P. (1963). The psychology of meaningful verbal learning: An introduction to school learning. New York, NY: Grune & Stratton.

    Google Scholar 

  • Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational psychology—a cognitive view. London: Holt, Rienhart and Winston.

    Google Scholar 

  • Bjork, R. A., & Linn, M. C. (2006). The science of learning and the learning of science—introducing desirable difficulties. The APS Observer, 19(3), 29, 39.

    Google Scholar 

  • Bransford, J., Brown, A. L., & Crocking, R. R. (2000a). How people learn: Brain, mind, experience, and school (expanded ed., pp. x, 374 p). Washington, DC: National Academy Press.

    Google Scholar 

  • Bransford, J. D., Brown, A. L., & Crocking, R. R. (2000b). How experts differ from novices. In How people learn: Brain, mind, experience, and school (expanded ed., Chap. 2). Washington, DC: National Academy Press.

    Google Scholar 

  • Bruner, J. S. (1960). The process of education. New York, NY: Vantage.

    Google Scholar 

  • Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1986). A study of thinking. New Brunswick, NJ: Transaction Publishers.

    Google Scholar 

  • Canas, A. J. (2003). A summary of literature pertaining to the use of concept mapping techniques and technologies for education and performance support. Http://www.ihmc.us/users/acanas/Publications/ConceptMapLitReview/

  • Canas, A. J. (2004). Cmap tools—knowledge modeling kit [Computer Software]. Pensacola, FL : Institute for Human and Machine Cognition (IHMC).

    Google Scholar 

  • Cathcart, L., Stieff, M., Marbach-Ad, G., Smith, A., & Frauwirth, K. (2010). Using knowledge structure maps as a foundation for knowledge management. ICLS.

    Google Scholar 

  • Chang, K. E., Chiao, B. C., Chen, S. W., & Hsiao, R. S. (2000). A programming learning system for beginners—a completion strategy approach. IEEE Transactions on Education, 43(2), 211–220.

    Article  Google Scholar 

  • Chang, K. E., Sung, Y. T., & Chen, S. F. (2001). Learning through computer-based concept mapping with scaffolding aid. Journal of Computer Assisted Learning, 17(1), 21–33.

    Article  Google Scholar 

  • Chartrand, G., & Zhang, P. (2004). Introduction to graph theory. Boston, MA: McGraw-Hill Higher Education.

    Google Scholar 

  • Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–151.

    Article  Google Scholar 

  • Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. S. Sternberg (Ed.). Advances in the psychology of human intelligence (Vol. 1, pp. 1–75). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Cline, B. E., Brewster, C. C., & Fell, R. D. (2009). A rule-based system for automatically evaluating student concept maps. Expert Systems with Applications, 37, 2282–2291.

    Article  Google Scholar 

  • Coleman, E. B. (1998). Using explanatory knowledge during collaborative problem solving in science. Journal of the Learning Sciences, 7(3), 387–427.

    Article  Google Scholar 

  • Czerniak, C. M., & Haney, J. J. (1998). The effect of collaborative concept mapping on elementary preservice teachers’ anxiety, efficacy, and achievement in physical science. Journal of Science Teacher Education, 9(4), 303–320.

    Article  Google Scholar 

  • Derbentseva, N., Safayeni, F., & Canas, A. J. (2007). Concept maps: Experiments on dynamic thinking. Journal of Research in Science Teaching, 44(3), 448–465.

    Article  Google Scholar 

  • Duncan, R. G., & Reiser, B. J. (2005). Designing for complex system understanding in the high school biology classroom. Annual Meeting of the National Association for Research in Science Teaching.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Edmondson, K. M. (2000). Assessing science understanding through concept maps. In J. J. Mintzes, J. H. Wandersee, & J. D. Novak (Eds.), Assessing science understanding: A human constructivist view. Educational psychology press (pp. 15–40). Elsevier Academic Press.

    Google Scholar 

  • Fisher, K. M. (2000). SemNet software as an assessment tool. In J. J. Mintzes, J. H. Wandersee, & J. D. Novak (Eds.), Assessing science understanding: A human constructivist view (pp. 197–221). Elsevier Academic Press.

    Google Scholar 

  • Fisher, K. M., Wandersee, J. H. M., & Moody, D. E. (2000). Mapping biology knowledge. Dordrecht, The Netherlands: Kluwer Academic Publishers.

    Google Scholar 

  • Gentner, D. (1978). On relational meaning: The acquisition of verb meaning. Child Development, 49, 988.

    Article  Google Scholar 

  • Glaser, R., Chi, M. T. H., & Farr, M. J. (1985). The nature of expertise (National Center for Research in Vocational Education). Columbus, OH: The Ohio State University.

    Google Scholar 

  • Goel, A., & Chandrasekaran, B. (1989). Functional representation of designs and redesign problem solving. In Proceedings of the 11th International Joint Conference on Artificial Intelligence—Volume 2 (pp. 1388–1394).

    Google Scholar 

  • Goel, A. K., Rugaber, S., & Vattam, S. (2008). Structure, behavior, and function of complex systems: The structure, behavior, and function modeling language. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 23, 23.

    Article  Google Scholar 

  • Grundspenkis, J., & Strautmane, M. (2009). Usage of graph patterns for knowledge assessment based on concept maps. Scientific Journal of Riga Technical University. Computer Sciences, 38(38), 60–71.

    Google Scholar 

  • Herl, H. E. (1999). Reliability and validity of a computer-based knowledge mapping system to measure content understanding. Computers in Human Behavior, 15(3–4), 315–333.

    Article  Google Scholar 

  • Herl, H. E., O’Neil, H. F. J., Chung, G. K., Dennis, R. A., & Lee, J. J. (1997, March). Feasibility of an on-line concept mapping construction and scoring system. Report: ED424233. 27pp.

    Google Scholar 

  • Hmelo-Silver, C. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138.

    Article  Google Scholar 

  • Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert–novice understanding of complex systems. Journal of the Learning Sciences, 16(3), 307–331.

    Article  Google Scholar 

  • Hoffman, R. R. (1998). How can expertise be defined? Implications of research from cognitive psychology. In R. Williams, W. Faulkner, & J. Fleck (Eds.), Exploring expertise (pp. 81–100). Edinburgh, Scotland: University of Edinburgh Press.

    Google Scholar 

  • Holley, C. D., Dansereau, D. F., & Harold, F. O. N. (1984). Spatial learning strategies: Techniques, applications, and related issues. New York, NY: Academic.

    Google Scholar 

  • Hoppe, H. U., Engler, J., & Weinbrenner, S. (2012). The impact of structural characteristics of concept maps on automatic quality measurement. In J. van Aalst, K. Thompson, M. J. Jacobson, & P. Reimann (Eds.), Proceedings of the 10th international conference of the learning sciences (ICLS). Sydney, Australia: ISLS.

    Google Scholar 

  • Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97. http://dx.doi.org/10.1007/s11423-008-9087-4. doi:10.1007/s11423-008-9087-4

  • Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331, 772–775.

    Article  Google Scholar 

  • Kinchin, I. M. (2000a). Concept mapping in biology. Journal of Biological Education, 34(2), 61–68.

    Article  Google Scholar 

  • Kinchin, I. M. (2000b). From ‘ecologist’ to ‘conceptual ecologist’: The utility of the conceptual ecology for teachers of biology. Journal of Biological Education, 34(4), 178–183.

    Article  Google Scholar 

  • Kinchin, I. M. (2001). If concept mapping is so helpful to learning biology, why aren’t we all doing it? International Journal of Science Education, 23(12), 1257–1269.

    Article  Google Scholar 

  • Kinchin, I. M., De-Leij, F. A. A. M., & Hay, D. B. (2005). The evolution of a collaborative concept mapping activity for undergraduate microbiology students. Journal of Further and Higher Education, 29(1), 1–14.

    Article  Google Scholar 

  • Kommers, P., & Lanzing, J. (1997). Students’ concept mapping for hypermedia design: Navigation through world wide web (WWW) space and self-assessment. Journal of Interactive Learning Research, 8(3–4), 421–455.

    Google Scholar 

  • Lambiotte, J. G., Dansereau, D. F., Cross, D. R., & Reynolds, S. B. (1989). Multirelational seminatic maps. Educational Psychology Review, 1(4), 331–367.

    Article  Google Scholar 

  • Linn, M. C., Chang, H.-Y., Chiu, J., Zhang, H., & McElhaney, K. (2010). Can desirable difficulties overcome deceptive clarity in scientific visualizations? In A. Benjamin (Ed.), Successful remembering and successful forgetting: A festschrift in honor of Robert A. Bjork. London, UK: Psychology Press.

    Google Scholar 

  • Liu, L., & Hmelo-Silver, C. E. (2009). Promoting complex systems learning through the use of conceptual representations in hypermedia. Journal of Research in Science Teaching, 46, 1023–1040.

    Article  Google Scholar 

  • Markham, K. M., Mintzes, J. J., & Jones, M. G. (1993, August 1–4). The structure and use of biological knowledge about mammals in novice and experienced students. Paper Presented at the Third International Seminar on Misconceptions and Educational Strategies in Science and Mathematics. Cornell University, Ithaca, NY.

    Google Scholar 

  • Markham, K. M., Mintzes, J. J., & Jones, M. G. (1994). The concept map as a research and evaluation tool: Further evidence of validity. Journal of Research in Science Teaching, 31(1), 91–101.

    Article  Google Scholar 

  • Mayr, E. (1988). Toward a new philosophy of biology. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • McClure, J. R., Sonak, B., & Suen, H. K. (1999). Concept map assessment of classroom learning: Reliability, validity, and logistical practicality. Journal of Research in Science Teaching, 36(4), 475–492.

    Article  Google Scholar 

  • Michael, R. S. (1995). The validity of concept maps for assessing cognitive structure. Dissertation Abstracts International Section A: Humanities and Social Sciences, 55(10-A), 3141.

    Google Scholar 

  • Mintzes, J. J., Wanderersee, J. H., & Novak, J. D. (2001). Assessing understanding in biology. Journal of Biological Education, 35, 118–124.

    Article  Google Scholar 

  • Mintzes, J. J., Wandersee, J. H., & Novak, J. D. (1997). Meaningful learning in science: The human constructivist perspective. In Handbook of academic learning: Construction of knowledge. The educational psychology series (pp. 405–447). (1)U North Carolina, Dept of Biological Science, Wilmington, NC, US, San Diego: Academic Press.

    Google Scholar 

  • Nesbit, J. C., & Adesope, O. O. (2006). Learning with concept and knowledge maps: A meta-analysis. Review of Educational Research, 76(3), 413–448.

    Article  Google Scholar 

  • Novak, J. D., & Canas, A. J. (2006). The theory underlying concept maps and how to construct them. Pensacola, FL: IHMC.

    Google Scholar 

  • Novak, J. D., & Gowin, D. B. (1984). Learning how to learn. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • O’Donnell, A. M., Dansereau, D. F., & Hall, R. H. (2002). Knowledge maps as scaffolds for cognitive processing. Educational Psychology Review, 14(1), 71–86.

    Article  Google Scholar 

  • Osmundson, E., Chung, G., Herl, H., & Klein, D. (1999). Knowledge mapping in the classroom: A tool for examining the development of students’ conceptual understandings. Los Angeles, CA: University of California Los Angeles.

    Google Scholar 

  • Pallant, A., & Tinker, R. F. (2004). Reasoning with atomic-scale molecular dynamic models. Journal of Science Education and Technology, 13(1), 51–66.

    Article  Google Scholar 

  • Pearsall, N., Skipper, J., & Mintzes, J. J. (1997). Knowledge restructuring in the life sciences: A longitudinal study of conceptual change in biology. Science Education, 81(2), 193–215.

    Article  Google Scholar 

  • Pemmaraju, S. V., & Skiena, S. S. (2003). Computational discrete mathematics: Combinatorics and graph theory with mathematica. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  • Pirnay-Dummer, P., & Ifenthaler, D. (2010). Automated knowledge visualization and assessment. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 77–115). New York, NY: Springer.

    Chapter  Google Scholar 

  • Plotnick, E. (1997). Concept mapping: A graphical system for understanding the relationship between concepts: An ERIC digest. Syracuse, NY: Clearinghouse on Information & Technology.

    Google Scholar 

  • Rice, D. C., Ryan, J. M., & Samson, S. M. (1998). Using concept maps to assess student learning in the science classroom: Must different methods compete? Journal of Research in Science Teaching, 35(10), 1103–1127.

    Article  Google Scholar 

  • Romance, N. R., & Vitale, M. R. (1999). Concept mapping as a tool for learning: Broadening the framework for student-centered instruction. College Teaching, 47(2), 74–79.

    Article  Google Scholar 

  • Ruiz-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), 30.

    Google Scholar 

  • Ruiz-Primo, M. A., Iverson, H., & Yin, Y. (2009). Towards the use of concept maps in large-scale assessments: Exploring the efficiency of two scoring methods. NARST conference.

    Google Scholar 

  • Ruiz-Primo, M. A., Schultz, S. E., Li, M., & Shavelson, R. J. (2001). Comparison of the reliability and validity of scores from two concept-mapping techniques. Journal of Research in Science Teaching, 38(2), 260–278.

    Article  Google Scholar 

  • Ruiz-Primo, M. A., Schultz, S. E., & Shavelson, R. J. (1997). Concept map-based assessment in science: Two exploratory studies. CSE Report, 436.

    Google Scholar 

  • Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33(6), 569–600.

    Article  Google Scholar 

  • Rye, J. A., & Rubba, P. A. (2002). Scoring concept maps: An expert map-based scheme weighted for relationships. School Science and Mathematics, 102(1), 33–44.

    Article  Google Scholar 

  • Safayeni, F., Derbentseva, N., & Canas, A. J. (2005). A theoretical note on concepts and the need for cyclic concept maps. Journal of Research in Science Teaching, 42(7), 741–766. doi:10.1002/tea.20074.

    Article  Google Scholar 

  • Scaife, M., & Rogers, Y. (1996). External cognition: How do graphical representations work? International Journal of Human Computer Studies, 45(2), 185–213.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1991). Literate expertise. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 172–194). Cambridge: Cambridge University Press.

    Google Scholar 

  • Schvaneveldt, R. W., Durso, F. T., Goldsmith, T. E., Breen, T. J., Cooke, N. M., Tucker, R. G., et al. (1985). Measuring the structure of expertise. International Journal of Man–machine Studies, 23, 699–728.

    Article  Google Scholar 

  • Schwendimann, B. A. (2007). Integrating interactive genetics visualizations into high school biology. In Annual meeting of the American Educational Research Association 2007. Chicago, IL.

    Google Scholar 

  • Schwendimann, B. A. (2011a). Integrating genotypic and phenotypic ideas of evolution through critique-focused concept mapping. In Annual meeting of the American Educational Research Association 2011. New Orleans, LA.

    Google Scholar 

  • Schwendimann, B. A. (2011b). Linking genotypic and phenotypic ideas of evolution through collaborative critique-focused concept mapping. In Proceedings of the 9th International Conference on Computer-Supported Collaborative Learning (CSCL). Hong Kong, China: CSCL Conference.

    Google Scholar 

  • Shavelson, R. J., Ruiz-Primo, M. A., & Wiley, E. W. (2005). Windows into the mind. Higher Education, 49(4), 413–430.

    Article  Google Scholar 

  • Stoddart, T., Abrams, R., Gasper, E., & Canaday, D. (2000). Concept maps as assessment in science inquiry learning—a report of methodology. International Journal of Science Education, 22(12), 1221–1246. Retrieved from Google Scholar.

    Article  Google Scholar 

  • Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (p. 825). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Weinstein, C. E., & Mayer, R. E. (1983). The teaching of learning strategies. Innovation Abstracts, 5(32).

    Google Scholar 

  • Yin, Y., Vanides, J., Ruiz-Primo, M. A., Ayala, C. C., & Shavelson, R. J. (2005). Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use. Journal of Research in Science Teaching, 42(2), 166–184.

    Article  Google Scholar 

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Acknowledgements

I wish to thank my Ph.D. advisor, Dr. Marcia C. Linn, for all her guidance and exceptional mentorship. I also thank my doctoral committee members, Dr. Randi A. Engle and Dr. Leslea J. Hlusko, for sharing their expertise, guidance, and support.

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  1. Faculty of Education and Social Work, The University of Sydney, Room 237, Education Building A35, Sydney, NSW, 2006, Australia

    Beat A. Schwendimann

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Correspondence to Beat A. Schwendimann .

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  1. Open Universities Australia, Melbourne, Victoria, Australia

    Dirk Ifenthaler

  2. CfBT Education Trust, Maktab Rendah Sains Mara Felda (Trolak), Sungkai, Perak, Malaysia

    Ria Hanewald

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Schwendimann, B.A. (2014). Making Sense of Knowledge Integration Maps. In: Ifenthaler, D., Hanewald, R. (eds) Digital Knowledge Maps in Education. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3178-7_2

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