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Identifying cross-domain distinguishing features of cognitive structure

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Abstract

Our research aims to identify domain-specific similarities and differences of externalized cognitive structures. Cognitive structure, also known as knowledge structure or structural knowledge, is conceived as the manner in which an individual organizes the relationships of concepts in memory. By diagnosing these structures precisely, even partially, the educator comes closer to influencing them through instructional settings and materials. Our assessment and analysis of cognitive structures is realized within the HIMATT tool, which automatically generates four quantitative indicators for the structural entities of written text or causal maps. In our study, participants worked on the subject domains biology, history, and mathematics. Results clearly indicate different structural and semantic features across the three subject domains. Additionally, we found that written texts and causal maps seem to represent different structure and content across the three subject domains when compared to an expert’s representation. We conclude with a general discussion, instructional implications and suggestions for future research.

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References

  • Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (2001). I-S-T 2000 R. Göttingen: Hogrefe.

    Google Scholar 

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Anzai, Y., & Yokoyama, T. (1984). Internal models in physics problem solving. Cognition and Instruction, 1(4), 397–450.

    Article  Google Scholar 

  • Baalmann, W. (1997). Schülervorstellungen zur Evolution. In H. E. Bayrhuber (Ed.), Biologieunterricht und Lebenswirklichkeit (pp. 163–167). Kiel: IPN.

    Google Scholar 

  • Baird, J. R., & White, R. T. (1982). A case study of learning styles in biology. International Journal of Science Education, 4(3), 325–337.

    Google Scholar 

  • Bayrhuber, H. E. (2001). Biowissenschaft in Schule und Öffentlichkeit. Kiel: IPN.

    Google Scholar 

  • Biglan, A. (1973). The characteristics of subject matter in different academic areas. Journal of Applied Psychology, 57(3), 195–203. doi:10.1037/h0034701.

    Article  Google Scholar 

  • Bliss, J. (1996). Piaget und Vygotsky: Ihre Bedeutung für das Lehren und Lernen der Naturwissenschaften. Zeitschrift für Didaktik der Naturwissenschaften, 2(3), 3–16.

    Google Scholar 

  • Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.

    Article  Google Scholar 

  • Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152.

    Article  Google Scholar 

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

    Google Scholar 

  • Chung, G. K. W. K., & Baker, E. L. (2003). An exploratory study to examine the feasibility of measuring problem-solving processes using a click-through interface. Journal of Technology, Learning and Assessment, 2(2). Available from http://www.jtla.org.

  • Clariana, R. B., & Wallace, P. E. (2007). A computer-based approach for deriving and measuring individual and team knowledge structure from essay questions. Journal of Educational Computing Research, 37(3), 211–227.

    Article  Google Scholar 

  • Clement, J. (1981). Student’s preconceptions in introductory mechanics. American Association of Physics Teachers, 50(1), 66–71.

    Google Scholar 

  • Courant, R., & Robbins, H. (2000). Was ist Mathematik?. Berlin: Springer.

    Google Scholar 

  • de Corte, F., Greer, B., & Verschaffel, L. (1996). Mathematics teaching and learning. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 491–549). New York: Macmillan.

    Google Scholar 

  • de Vries, E. (2006). Students’ construction of external representations in design-based learning situations. Learning and Instruction, 16(3), 213–227. doi:10.1016/j.learninstruc.2006.03.006.

    Article  Google Scholar 

  • Donovan, M. S., & Bransford, J. D. (Eds.). (2005). How students learn. History, mathematics, and science in the classroom. Washington, D.C.: The National Academic Press.

    Google Scholar 

  • Dummer, P., & Ifenthaler, D. (2005). Planning and assessing navigation in model-centered learning environments. Why learners often do not follow the path laid out for them. In G. Chiazzese, M. Allegra, A. Chifari, & S. Ottaviano (Eds.), Methods and technologies for learning (pp. 327–334). Sothhampton: WIT Press.

    Google Scholar 

  • Durso, F. T., & Coggins, K. A. (1990). Graphs in social and psychological sciences: Empirical contributions to Pathfinder. In R. W. Schvaneveldt (Ed.), Pathfinder associative networks: Studies in knowledge organization (pp. 31–51). Norwood, NJ: Ablex Publishing Corportion.

    Google Scholar 

  • Ennis, R. H. (1989). Critical thinking and subject specificity: Clarification and needed research. Educational Researcher, 18(4), 4–10.

    Article  Google Scholar 

  • Ennis, R. H. (1990). The extent to which critical thinking is subject-specific: Further clarification. Educational Researcher, 19(13), 13–16.

    Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.

    Google Scholar 

  • Eschenhagen, D., Kattmann, U., & Rodi, D. (2008). Fachdidaktik Biologie. Köln: Aulis Verlag Deubner.

    Google Scholar 

  • Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378–382.

    Article  Google Scholar 

  • Gentner, D., & Stevens, A. L. (1983). Mental models. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306–355.

    Article  Google Scholar 

  • Glaser, R. (1999). Expert knowledge and processes of thinking. In R. McCormick & C. Paechter (Eds.), Learning and knowledge (pp. 88–102). Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  • Gruber, H. (1994). Expertise. Opladen: Westdeutscher Verlag.

    Google Scholar 

  • Gruber, H., & Ziegler, A. (1996). Expertiseforschung. Theoretische und methodische Grundlagen. Opladen: Westdeutscher Verlag.

    Google Scholar 

  • Hasberg, W. (2001). Empirische Forschung in der Geschichtsdidaktik. Neuried: ars una.

    Google Scholar 

  • Herl, H. E., Baker, E. L., & Niemi, D. (1996). Construct validation of an approach to modeling cognitive structure of U.S. history knowledge. Journal of Educational Research, 89(4), 206–218.

    Article  Google Scholar 

  • Hilbert, T. S., & Renkl, A. (2008). Concept mapping as a follow-up strategy to learning from texts: What characterizes good and poor mappers? Instructional Science, 36, 53–73.

    Article  Google Scholar 

  • Holley, K. (2009). The challenge of an interdisciplinary curriculum: A cultural analysis of a doctoral-degree program in neuroscience. Higher Education, 58(2), 241–255. doi:10.1007/s10734-008-9193-6.

    Article  Google Scholar 

  • Ifenthaler, D. (2008). Practical solutions for the diagnosis of progressing mental models. In D. Ifenthaler, P. Pirnay-Dummer, & J. M. Spector (Eds.), Understanding models for learning and instruction. Essays in honor of Norbert M. Seel (pp. 43–61). New York: Springer.

    Chapter  Google Scholar 

  • Ifenthaler, D. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and Learning, 7(2), 83–101.

    Google Scholar 

  • Ifenthaler, D. (2010a). Bridging the gap between expert-novice differences: The model-based feedback approach. Journal of Research on Technology in Education, 43(2), 103–117.

    Google Scholar 

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

    Article  Google Scholar 

  • Ifenthaler, D. (2010c). Scope of graphical indices in educational diagnostics. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 213–234). New York: Springer.

    Chapter  Google Scholar 

  • Ifenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how we can detect it. Tracking the development of cognitive structures over time. Instructional Science, 39(1), 41–61. doi:10.1007/s11251-009-9097-6.

    Article  Google Scholar 

  • Ifenthaler, D., & Pirnay-Dummer, P. (2009). Assessment of knowledge: Do graphical notes and texts represent different things? In M. R. Simonson (Ed.), Annual proceedings of selected research and development papers presented at the national convention of the Association for Educational Communications and Technology (32nd, Louisville, KY, 2009) (Vol. 2, pp. 86–93). Bloomington, IN: AECT.

  • Ifenthaler, D., Pirnay-Dummer, P., & Seel, N. M. (2007). The role of cognitive learning strategies and intellectual abilities in mental model building processes. Technology, Instruction, Cognition and Learning, 5(4), 353–366.

    Google Scholar 

  • Ifenthaler, D., & Schmidt, T. (2010). Assessing the effectiveness of prompts for self-regulated learning. In Kinshuk, D. G. Sampson, J. M. Spector, P. Isaias, D. Ifenthaler, & R. Vasiu (Eds.), Proceedings of the IADIS international conference on cognition and exploratory learning in the digital age (pp. 193–202). Timisoara: IADIS Press.

    Google Scholar 

  • Iggers, G. G. (1996). Geschichtswissenschaft im 20. Jahrhundert. Göttingen: Vandenhoeck und Ruprecht.

    Google Scholar 

  • Johnson, J., McKee, S., & Vella, A. (Eds.). (1994). Artificial intelligence in mathematics. New York: Oxford University Press.

    Google Scholar 

  • Johnson-Laird, P. N. (1983). Mental models. Towards a cognitive science of language, inference, and consciousness. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Johnson-Laird, P. N. (1989). Mental models. In M. I. Posner (Ed.), Foundations of cognitive science (pp. 469–499). Cambridge, MA: MIT Press.

    Google Scholar 

  • Johnson-Laird, P. N., & Byrne, R. (1991). Deduction. Hove: Lawrence Erlbaum.

    Google Scholar 

  • Jonassen, D. H. (1987). Assessing cognitive structure: Verifying a method using pattern notes. Journal of Research and Development in Education, 20(3), 1–14.

    Google Scholar 

  • Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hilsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Jonassen, D. H., & Cho, Y. H. (2008). Externalizing mental models with mindtools. In D. Ifenthaler, P. Pirnay-Dummer, & J. M. Spector (Eds.), Understanding models for learning and instruction. Essays in honor of Norbert M. Seel (pp. 145–160). New York: Springer.

    Chapter  Google Scholar 

  • Kim, H. (2008). An investigation of the effects of model-centered instruction in individual and collaborative contexts: The case of acquiring instructional design expertise. Tallahassee, FL: Florida State University.

    Google Scholar 

  • Kitcher, P. (1983). The nature of mathematical knowledge. Oxford: Oxford University Press.

    Google Scholar 

  • Kleinert, E. (2005). Drei Studien zur Struktur der Mathematik. Hamburger Beiträge zur Mathematik, 229, 1–66.

    Google Scholar 

  • Koubek, R. J., Clarkston, T. P., & Calvez, V. (1994). The training of knowledge structures for manufacturing tasks: An empirical study. Ergonomics, 37(4), 765–780.

    Article  Google Scholar 

  • Ku, W. A. (2007). Using concept maps to explore the conceptual knowledge of technology students: An exploratory study. Columbus, OH: Ohio State University.

    Google Scholar 

  • Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9(4), 285–327.

    Article  Google Scholar 

  • Lachner, A., & Pirnay-Dummer, P. (2010). Model-based knowledge mapping. In J. M. Spector, D. Ifenthaler, P. Isaias, Kinshuk, & D. G. Sampson (Eds.), Learning and instruction in the digital age (pp. 69–86). New York: Springer.

    Chapter  Google Scholar 

  • Lee, J. (2009). Effects of model-centered instruction and levels of learner expertise on effectiveness, efficiency, and engagement with ill-structured problem solving: An exploratory study of ethical decision making in program evaluation. Tallahassee, FL: Florida State University.

    Google Scholar 

  • Lehrer, R., & Romberg, T. (1996). Exploring children′s data modeling. Cognition and Instruction, 14(1), 69–108.

    Article  Google Scholar 

  • McKeown, J. O. (2009). Using annotated concept map assessments as predictors of performance and understanding of complex problems for teacher technology integration. Tallahassee, FL: Florida State University.

    Google Scholar 

  • McPeck, J. E. (1990). Critical thinking and subject specificity: A reply to Ennis. Educational Researcher, 19(10), 10–12.

    Google Scholar 

  • Mikkilä-Erdmann, M., Penttinen, M., Anto, E., & Olkinuora, E. (2008). Constructing mental models during learning from science text. Eye tracking methodology meets conceptual change. In D. Ifenthaler, P. Pirnay-Dummer, & J. M. Spector (Eds.), Understanding models for learning and instruction. Essays in honor of Norbert M. Seel (pp. 63–79). New York: Springer.

    Chapter  Google Scholar 

  • Mintzes, J. J., Yen, C., & Barney, E. C. (2008). Assessing knowledge, attitudes, and behavior towards charismatic megafauna. The case of dolphins. Journal of Environmental Education, 36(2), 41–55.

    Google Scholar 

  • Mirow, J. (1991). Geschichtswissen durch Geschichtsunterricht? Historische Kenntnisse und ihr Erwerb innerhalb und außerhalb der Schule. In B. von Borries, H. Pandel, & J. Rüsen (Eds.), Geschichtsbewußtsein empirisch (pp. 53–109). Pfaffenweiler: Centaurus-Verlagsgesellschaft.

    Google Scholar 

  • Moeira, M. A. (1983). Assessment of content and cognitive structures in physics at college level. Assessment and Evaluation in Higher Education, 8(3), 234–245.

    Article  Google Scholar 

  • Nason, A., & Goldstein, P. (1969). Biology; introduction to life. Menlo Park, CA: Addison-Wesley.

    Google Scholar 

  • Nikitina, S. (2005). Pathways of interdisciplinary cognition. Cognition and Instruction, 23(3), 389–425, doi:10.1207/s1532690xci2303_3.

    Google Scholar 

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

    Article  Google Scholar 

  • Pandel, H. (1987). Dimensionen des Geschichtsbewusstseins. Ein Versuch, seine Struktur für Empirie und Pragmatik diskutierbar zu machen. Geschichtsdidaktik, 12(2), 130–142.

    Google Scholar 

  • Pape, M. (2006). Methodische Zugangsweisen zur Erfassung von Geschichtsbewusstsein im Kindesalter: Gruppendiskussionen und Kinderzeichnungen. In G. Hilke & M. Sauer (Eds.), Geschichtsdidaktik empirisch - Untersuchungen zum historischen Denken und Lernen (pp. 85–110). München: LIT Verlag.

    Google Scholar 

  • Piaget, J. (1972). Das mathematische Denken. Stuttgart: Klett.

    Google Scholar 

  • Piaget, J. (1976). Die Äquilibration der kognitiven Strukturen. Stuttgart: Klett.

    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: Springer.

    Chapter  Google Scholar 

  • Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research and Development, 58(1), 3–18. doi:10.1007/s11423-009-9119-8.

    Article  Google Scholar 

  • Rips, L. J. (1994). The psychology of proof: Deductive reasoning in human thinking. Cambridge, MA: MIT Press.

    Google Scholar 

  • Rüsen, J., Fröhlich, K., Horstkötter, H., & Schmidt, H. G. (1991). Untersuchungen zum Geschichtsbewußtsein von Abiturienten im Ruhrgebiet. In B. von Borries, H. Pandel, & J. Rüsen (Eds.), Geschichtsbewußtsein empirisch (pp. 221–344). Pfaffenweiler: Centaurus-Verlagsgesellschaft.

    Google Scholar 

  • Ryle, G. (1949). The concept of mind. London: Hutchinson.

    Google Scholar 

  • Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Student′s transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28, 859–882.

    Google Scholar 

  • Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156. doi:10.1016/S0959-4752(02)00017-8.

    Article  Google Scholar 

  • Schvaneveldt, R. W. (1990). Pathfinder associative networks: Studies in knowledge organization. Norwood: NJ: Ablex Publishing Corporation.

    Google Scholar 

  • Seel, N. M. (1999a). Educational diagnosis of mental models: Assessment problems and technology-based solutions. Journal of Structural Learning and Intelligent Systems, 14(2), 153–185.

    Google Scholar 

  • Seel, N. M. (1999b). Educational semiotics: School learning reconsidered. Journal of Structural Learning and Intelligent Systems, 14(1), 11–28.

    Google Scholar 

  • Seel, N. M., Ifenthaler, D., & Pirnay-Dummer, P. (2009). Mental models and problem solving: Technological solutions for measurement and assessment of the development of expertise. In P. Blumschein, W. Hung, D. H. Jonassen, & J. Strobel (Eds.), Model-based approaches to learning: Using systems models and simulations to improve understanding and problem solving in complex domains (pp. 17–40). Rotterdam: Sense Publishers.

    Google Scholar 

  • Shavelson, R. J. (1972). Some aspects of the correspondence between content structure and cognitive structure in Physics education. Journal of Educational Psychology, 63(3), 225–234.

    Article  Google Scholar 

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.

    Article  Google Scholar 

  • Smith, L. J. (2009). Graph and property set analysis: A methodology for comparing mental model representations. Tallahassee, FL: Florida State University.

    Google Scholar 

  • Snow, R. E. (1989). Toward assessment of cognitive and conative structures in learning. Educational Researcher, 18(9), 8–14.

    Google Scholar 

  • Snow, R. E. (1990). New approaches to cognitive and conative assessment in education. International Journal of Educational Research, 14(5), 455–473.

    Google Scholar 

  • Spector, J. M. (2010). Mental representations and their analysis: An epestimological perspective. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 27–40). New York: Springer.

    Chapter  Google Scholar 

  • Spector, J. M., Dennen, V. P., & Koszalka, T. A. (2006). Causal maps, mental models and assessing acquisition of expertise. Technology, Instruction, Cognition and Learning, 3(2), 167–183.

    Google Scholar 

  • Spector, J. M., & Koszalka, T. A. (2004). The DEEP methodology for assessing learning in complex domains (final report to the national science foundation evaluative research and evaluation capacity building). Syracuse, NY: Syracuse University.

    Google Scholar 

  • Sternberg, R. J. (1993). Giftedness as developing expertise. In K. A. Heller, F. J. Mönks, R. J. Sternberg, & R. F. Subotnik (Eds.), International handbook of giftedness and talent (pp. 55–66). Oxford: Pergamon.

    Google Scholar 

  • Strasser, A. (2010). A functional view toward mental representations. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge (pp. 15–26). New York: Springer.

    Chapter  Google Scholar 

  • Taber, K. S. (1995). Development of student understanding: A case study of stability and lability in cognitive structure. Research in Science and Technological Education, 13(1), 89–99.

    Article  Google Scholar 

  • Tamir, P., & Jungwirth, E. (1972). Teaching objectives in biology: Priorities and expectations. Science Education, 56(1), 31–39.

    Article  Google Scholar 

  • Thompson, T. L., & Mintzes, J. J. (2002). Cognitive structure and the affective domain: On knowing and feeling in biology. Journal of Science Education, 24(6), 645–660.

    Article  Google Scholar 

  • Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352.

    Article  Google Scholar 

  • von Borries, B. (2001). Lehr- und Lernforschung im Fach Geschichte. In W. Gerhard (Ed.), Lehren und Lernen im Kontext empirischer Forschung und Fachdidaktik (pp. 399–438). Donau-Wörth: Auer.

    Google Scholar 

  • Voss, J. F., Greece, T. R., Post, T. A., & Penner, B. C. (1983). Problem-solving skill in the social sciences. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory. New York: Academic Press.

    Google Scholar 

  • Vye, N. J., Goldman, S. R., Voss, J. F., Hmelo, C., & Williams, S. (1997). Complex mathematical problem solving by individuals and dyads. Cognition and Instruction, 15(4), 435–484.

    Article  Google Scholar 

  • Watts, M. (1988). From concept maps to curriculum signposts. Physics Education, 23, 74–79.

    Article  Google Scholar 

  • Winter, H. (1975). Allgemeine Lehrziele im Mathematikunterricht. Zentralblatt für Didaktik der Mathematik, 3, 106–116.

    Google Scholar 

  • Wolfe, M. B. W., & Goldman, S. R. (2005). Relations between adolescents’ text processing and reasoning. Cognition and Instruction, 23(4), 467–502.

    Article  Google Scholar 

  • Woods, C. (2007). Researching and developing interdisciplinary teaching: Towards a conceptual framework for classroom communication. Higher Education, 54(6), 853–866. doi:10.1007/s10734-006-9027-3.

    Article  Google Scholar 

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Ifenthaler, D. Identifying cross-domain distinguishing features of cognitive structure. Education Tech Research Dev 59, 817–840 (2011). https://doi.org/10.1007/s11423-011-9207-4

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