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The mystery of cognitive structure and how we can detect it: tracking the development of cognitive structures over time

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Abstract

Many research studies have clearly demonstrated the importance of cognitive structures as the building blocks of meaningful learning and retention of instructional materials. Identifying the learners’ cognitive structures will help instructors to organize materials, identify knowledge gaps, and relate new materials to existing slots or anchors within the learners’ cognitive structures. The purpose of our empirical investigation is to track the development of cognitive structures over time. Accordingly, we demonstrate how various indicators derived from graph theory can be used for a precise description and analysis of cognitive structures. Our results revealed several patterns that helped us to better understand the construction and development of cognitive structures over time. We conclude by identifying applications of our approach for learning and instruction and proposing possibilities for the further development of our approach.

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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. doi:10.1037/0022-0663.86.2.303.

    Article  Google Scholar 

  • Al-Diban, S. (2002). Diagnose mentaler Modelle. Hamburg: Verlag Dr. Kovac.

    Google Scholar 

  • Ausubel, D. P. (1963). Cognitive structure and the facilitation of meaningful verbal learning. Journal of Teacher Education, 14, 217–221. doi:10.1177/002248716301400220.

    Article  Google Scholar 

  • Bonato, M. (1990). Wissenstrukturierung mittels Struktur-Lege-Techniken. Eine grapentheoretische Analyse von Wissensnetzen. Frankfurt am Main: Lang.

    Google Scholar 

  • Cañas, A. J., Hill, R., Carff, R., Suri, N., Lott, J., & Eskridge, T. (2004). CmapTools: A knowledge modeling and sharing environment. In A. J. Cañas, J. D. Novak, F. M. González, et al. (Eds.), Concept maps: Theory, methodology, technology, Proceedings of the first international conference on concept mapping (pp. 125–133). Pamplona: Universidad Pública de Navarra.

    Google Scholar 

  • Chartrand, G. (1977). Introductory graph theory. New York: Dover.

    Google Scholar 

  • 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. doi:10.2190/EC.37.3.a.

    Article  Google Scholar 

  • Coffey, J. W., Carnot, M. J., Feltovich, P. J., Feltovich, J., Hoffman, R. R., Cañas, A. J., et al. (2003). A summary of literature pertaining to the use of concept mapping techniques and technologies for education and performance support. Pensacola, FL: Chief of Naval Education and Training.

    Google Scholar 

  • Collins, L. M., & Sayer, A. G. (Eds.). (2001). New methods for the analysis of change. Washington, DC: American Psychological Association.

    Google Scholar 

  • Derbentseva, N., Safayeni, F., & Cañas, A. J. (2004). Experiments on the effects of map structure and concept quantification during concept map construction. In A. J. Cañas, J. D. Novak & F. M. González (Eds.), Concept maps: Theory, methodology, technology, Proceedings of the First International Conference on Concept Mapping (pp. 125–132). Pamplona: Universidad Pública de Navarra.

  • Diestel, R. (2000). Graph theory. New York: Springer.

    Google Scholar 

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

    Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1998). How to study thinking in everyday life. Mind, Culture, and Activity, 5(3), 178–186. doi:10.1207/s15327884mca0503_3.

    Article  Google Scholar 

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

    Google Scholar 

  • Gunstone, R. F. (1980). Word association and the description of cognitive structure. Research in Science Education, 10, 45–53. doi:10.1007/BF02356308.

    Article  Google Scholar 

  • Harary, F. (1974). Graphentheorie. München: Oldenbourg.

    Google Scholar 

  • Harris, C. W. (Ed.). (1963). Problems in measuring change. Madison, WI: The University of Wisconsin Press.

    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. The Journal of Educational Research, 89(4), 206–218.

    Article  Google Scholar 

  • Hox, J. (2002). Multilevel analysis. Techniques and applications. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Ifenthaler, D. (2006). Diagnose lernabhängiger Veränderung mentaler Modelle. Entwicklung der SMD-Technologie als methodologisches Verfahren zur relationalen, strukturellen und semantischen Analyse individueller Modellkonstruktionen. Freiburg: FreiDok.

    Google Scholar 

  • Ifenthaler, D. (2008a). 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. (2008b). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development. doi:10.1007/s11423-008-9087-4.

  • Ifenthaler, D. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and learning, (in press).

  • Ifenthaler, D., & Seel, N. M. (2005). The measurement of change: Learning-dependent progression of mental models. Technology, Instruction, Cognition and Learning, 2(4), 317–336.

    Google Scholar 

  • Johnson, T. E., O’Connor, D. L., Spector, J. M., Ifenthaler, D., & Pirnay-Dummer, P. (2006). Comparative study of mental model research methods: Relationships among ACSMM, SMD, MITOCAR & DEEP methodologies. In A. J. Cañas & J. D. Novak (Eds.), Concept maps: Theory, methodology, technology. Proceedings of the Second International Conference on Concept Mapping, Voume 1 (pp. 87–94). San José: Universidad de Costa Rica.

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

    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. (1988). Designing structured hypertext and structuring access to hypertext. Educational Technology, 28(11), 13–16.

    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 

  • Kalyuga, S. (2006a). Assessment of learners’ organised knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20, 333–342. doi:10.1002/acp.1249.

    Article  Google Scholar 

  • Kalyuga, S. (2006b). Rapid assessment of learners’ proficiency: A cognitive load approach. Educational Psychology, 26(6), 735–749. doi:10.1080/01443410500342674.

    Article  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. doi:10.1080/00140139408963687.

    Article  Google Scholar 

  • Koubek, R. J., & Mountjoy, D. N. (1991). Toward a model of knowledge structure and a comparative analysis of knowledge structure measurement technique. West Lafayette, IN: Purdue University.

    Google Scholar 

  • Mayer, R. E., & Greeno, J. G. (1972). Structural differences between learning outcomes produced by different instructional methods. Journal of Educational Psychology, 63(2), 165–173. doi:10.1037/h0032654.

    Article  Google Scholar 

  • Moskowitz, D. S., & Hershberger, S. L. (Eds.). (2002). Modelling intraindividual variability with repeated measures data. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84, 231–259. doi:10.1037/0033-295X.84.3.231.

    Article  Google Scholar 

  • Norman, D. A., Gentner, D. R., & Stevens, A. L. (1976). Comments on learning schemata and memory representation. In D. Klahr (Ed.), Cognition and instruction (pp. 177–196). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Novak, J. D. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

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

    Google Scholar 

  • Pirnay-Dummer, P. (2006). Expertise und Modellbildung: MITOCAR. Freiburg: FreiDok.

    Google Scholar 

  • Pirnay-Dummer, P. (2007). Model inspection trace of concepts and relations. A heuristic approach to language-oriented model assessment. Paper presented at the AREA 2007, Chicago, IL.

  • Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2008). Highly integrated model assessment technology and tools. In Kinshuk, Sampson, D. G., Spector, J. M., Isaias P. & Ifenthaler D. (Eds.), Proceedings of the IADIS international conference on cognition and exploratory learning in the digital age (pp. 18–28). Freiburg: IADIS.

  • Preece, P. F. W. (1976). Mapping cognitive structure: A comparison of models. Journal of Educational Psychology, 68(1), 1–8. doi:10.1037/0022-0663.68.1.1.

    Article  Google Scholar 

  • Quillian, M. R. (1968). Semantic memory. In M. Minsky (Ed.), Semantic information processing (pp. 216–270). Cambridge, MA: MIT Press.

    Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models. Applications and data analysis methods. Thousand Oaks, CA: SAGE Publications.

    Google Scholar 

  • Renkl, A., & Gruber, H. (1995). Erfasung von Veränderung: Wie und wieso? Zeitschrift fur Entwicklungspsychologie und Padagogische Psychologie, 27(2), 173–190.

    Google Scholar 

  • Rumelhart, D. E., & Norman, D. A. (1978). Accretion, tuning and restructuring: Three model of learning. In R. L. Klatzky & J. W. Cotton (Eds.), Semantic factors in cognition (pp. 37–53). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Scheele, B., & Groeben, N. (1984). Die Heidelberger Struktur-Lege-Technik (SLT). Eine Dialog-Konsens-Methode zur Erhebung subjektiver Theorien mittlerer Reichweite. Weinheim: Beltz.

    Google Scholar 

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

    Google Scholar 

  • Seel, N. M. (1991). Weltwissen und mentale Modelle. Göttingen: Hogrefe.

    Google Scholar 

  • Seel, N. M. (1999). 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. (2001). Epistemology, situated cognition, and mental models: ‘Like a bridge over troubled water’. Instructional Science, 29(4–5), 403–427. doi:10.1023/A:1011952010705.

    Article  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. doi:10.1037/h0032652.

    Article  Google Scholar 

  • Shavelson, R. J. (1974). Methods for examining representations of a subject-matter structure in student memory. Journal of Research in Science Teaching, 11(3), 231–249. doi:10.1002/tea.3660110307.

    Article  Google Scholar 

  • Shavelson, R. J., & Stanton, G. C. (1975). Construct validation: Methodology and application to three measures of cognitive structure. Journal of Educational Measurement, 12(2), 67–85. doi:10.1111/j.1745-3984.1975.tb01010.x.

    Article  Google Scholar 

  • Shute, V. J., & Zapata-Rivera, D. (2008). Using an evidence-based approach to assess 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. 23–42). New York: Springer.

    Chapter  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 

  • Snow, R. E., & Lohman, D. F. (1989). Implications of cognitive psychology for educational measurement. In R. L. Linn (Ed.), Educational measurement (pp. 263–331). New York: ACE/Macmillan.

    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.

  • Taber, K. S. (2000). Multiple frameworks?: Evidence of manifold conceptions in individual cognitive structure. International Journal of Science Education & Training, 22(4), 399–417.

    Google Scholar 

  • Tennyson, R. D., & Cocchiarella, M. J. (1986). An empirically based instructional design theory for teaching concepts. Review of Educational Research, 56(1), 40–71.

    Google Scholar 

  • Willett, J. B. (1988). Questions and answers in the measurement of change. Review of Research in Education, 15, 345–422.

    Google Scholar 

  • Young, M. J. (1998). Quantifying the characteristics of knowledge structure representations: A lattice-theoretic framework. Los Angeles, CA: CRESST.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Albert-Ludwigs-University Freiburg, Freiburg, Germany

    Dirk Ifenthaler & Norbert M. Seel

  2. Florida State University, Tallahassee, FL, USA

    Iskandaria Masduki

Authors
  1. Dirk Ifenthaler
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  2. Iskandaria Masduki
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  3. Norbert M. Seel
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Correspondence to Dirk Ifenthaler.

Appendices

Appendix 1

  • H1.1: the organization of the externalized cognitive structures changes during the learning process.

  • H1.0: the organization of the externalized cognitive structures does not change during the learning process.

  • H2.1a: the numbers of semantic correct vertices of the externalized cognitive structures become more similar to the expert structure during the learning process.

  • H2.0a: the numbers of semantic correct vertices of the externalized cognitive structures have no or only little similarity to the expert structure.

  • H2.1b: the numbers of semantic correct propositions of the externalized cognitive structures become more similar to the expert structure during the learning process.

  • H2.0b: the numbers of semantic correct propositions of the externalized cognitive structures have no or only little similarity to the expert structure.

  • H3.1: the development of the organization of the externalized cognitive structures influences the course learning outcomes.

  • H3.0: the development of the organization of the externalized cognitive structures has no or only little influence on the course learning outcomes.

Appendix 2

See Tables 9 and 10.

Table 9 Level-2 linear growth models of cognitive structures (organization) and course learning outcomes
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Table 10 Level-2 linear growth models of cognitive structures (semantic content) and course learning outcomes
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Ifenthaler, D., Masduki, I. & Seel, N.M. The mystery of cognitive structure and how we can detect it: tracking the development of cognitive structures over time. Instr Sci 39, 41–61 (2011). https://doi.org/10.1007/s11251-009-9097-6

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