SpringerLink
Log in

The influence of external concept structures on an individual’s knowledge structures

  • Research Article
  • Published:
Educational technology research and development Aims and scope Submit manuscript

Abstract

This experimental investigation considers how the inherent conceptual structure of external representations influences individuals' knowledge structure, and in addition proposes a measure of global collective knowledge to account for the influence of pre-existing knowledge structure. In two studies, undergraduates in a hospitality management course completed a pre-knowledge structure (pre KS) measure, a prior knowledge pretest, then read parallel versions of either a text or a table about the Internet of Things, then completed a post knowledge structure (post KS) measure, and finally completed a comprehension posttest. Analysis of the comprehension posttest data showed that the text group significantly outperformed the table group (p < .05) mainly due to performance on factual and main idea items, but not inference items. The pre- and post-KS data were analyzed as Pathfinder networks. Descriptive comparisons of between group networks (group–group) and within group networks (pre-post) showed that the table and text between-group networks were quite alike before reading and were even more alike after reading (i.e., peer convergence of local collective knowledge structure). The within-group network overlap from pre-to-post was also substantial. In addition, pre-to-post similarity with the expert shows the text group networks became more like the expert referent but the table group networks became less like the expert referent. Exploratory findings for this global collective knowledge network approach based on Google Ngram frequency dependencies were partially supported. For theory building, the results show how the influence of external representations can be framed in terms of a representation's inherent conceptual structure. For practice, this list-wise measure for eliciting knowledge structure provides a quick way to elicit individual and group-level knowledge structure networks that can be used in ordinary classrooms for formative and summative assessment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Clariana, R. B. (2003). The effectiveness of constructed-response and multiple-choice study tasks in computer aided learning. Journal of Educational Computing Research, 28(4), 395–406.

    Article  Google Scholar 

  • Clariana, R. B. (2010). Deriving individual and group knowledge structure from network diagrams and from essays. Computer-based diagnostics and systematic analysis of knowledge (pp. 117–130). Springer.

    Chapter  Google Scholar 

  • Clariana, R. B., Follmer, D. J., & Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. In Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17–19, 2019, University of Jyväskylä, Finland. Retrievedfrom https://www.slideshare.net/rbc4/sentence-versus-paragraphprocessing-linear-and-relational-knowledge-structure-measures

  • Clariana, R. B., & Wallace, P. (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 

  • Clariana, R. B., Tang, H., & Chen, X. (2022). Corroborating a sorting task measure of individual and of local collective knowledge structure. Educational Technology Research and Development. https://doi.org/10.1007/s11423-022-10123-x

    Article  Google Scholar 

  • Corradi, D., Trinenuh, D. T., Clarebout, G., & Elen, J. (2014). How multiple external representations can help or constrain learning in science. Journal of Cognitive Education and Psychology, 13(3), 411–423. https://doi.org/10.1891/1945-8959.13.3.411

    Article  Google Scholar 

  • Einstein, G. O., McDaniel, M. A., Bowers, C. A., & Stevens, D. T. (1984). Memory for prose: The influence of relational and proposition-specific processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(1), 133–143. https://doi.org/10.1037/0278-7393.10.1.133

    Article  Google Scholar 

  • Fiorella, L., & Mayer, R. E. (2016). Eight ways to promote generative learning. Educational Psychology Review, 28, 717–741. https://doi.org/10.1007/s10648-015-9348-9

    Article  Google Scholar 

  • Frase, L. T. (1969). Paragraph organization of written materials: The influence of conceptual clustering upon the level and organization of recall. Journal of Educational Psychology, 60(5), 394–401.

    Article  Google Scholar 

  • Gagné, E. D. (1985). The cognitive psychology of school learning. Little, Brown and Company.

    Google Scholar 

  • Gogus, A. (2013). Evaluating mental models in mathematics: A comparison of methods. Educational Technology, Research & Development, 61(2), 171–195. https://doi.org/10.1007/s11423-012-9281-2

    Article  Google Scholar 

  • Goodman, K. S. (1986). What’s whole in whole language? A parent/teacher guide to children’s learning. Heinemann Educational Books Inc.

    Google Scholar 

  • Hecker, A. (2012). Knowledge beyond the individual? Making sense of a notion of collective knowledge in organization theory. Organization Studies, 33, 423–445. https://doi.org/10.1177/0170840611433995

    Article  Google Scholar 

  • Ideno, T., Morii, M., Takemura, K., & Okada, M. (2020). On effects of changing multi-attribute table design on decision making: An eye-tracking study. In A. V. Pietarinen, P. Chapman, S. L. Bosveld-de, V. Giardino, J. Corter, & S. Linker (Eds.), Diagrammatic representation and inference diagrams. 2020. Lecture notes in computer science (p. 12169). Springer.

    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. https://doi.org/10.1007/s11423-008-9087-4

    Article  Google Scholar 

  • 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, 353–366.

    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. Lawrence Erlbaum Associates.

    Google Scholar 

  • Kennedy, J. J., & Bush, A. J. (1985). An introduction to the design and analysis of experiments in behavioral research. University Press of America.

    Google Scholar 

  • Kim, K. (2019). Graphical Interface of Knowledge Structure: A web-based research tool for representing KS in text. Technology Knowledge and Learning, 24, 89–95. https://doi.org/10.1007/s10758-017-9321-4

    Article  Google Scholar 

  • Kim, K., & Clariana, R. B. (2015). Knowledge structure measures of reader’s situation models across languages: Translation engenders richer structure. Technology, Knowledge and Learning, 20(2), 249–268.

    Article  Google Scholar 

  • Kim, K., & Clariana, R. B. (2017). Text signals influence second language expository text comprehension: Knowledge structure analysis. Educational Technology Research and Development, 65(4), 909–930.

    Article  Google Scholar 

  • Kintsch, W. (1992). A cognitive architecture for comprehension. In H. L. Pick, P. W. van den Broek, & D. C. Knill (Eds.), Cognition: Conceptual and methodological issues (pp. 143–163). American Psychological Association.

    Chapter  Google Scholar 

  • Kintsch, W., & Yarbrough, J. C. (1982). Role of rhetorical structure in text comprehension. Journal of Educational Psychology, 74(6), 828–834. https://doi.org/10.1037/0022-0663.74.6.828

    Article  Google Scholar 

  • Kollar, I., & Fischer, F. (2004). The interaction between internal and external collaboration scripts in computer-supported collaborative learning. In S. Demetriadis (Ed.), Interaction between learner’s internal and external representations in multimedia environment: a state-of-the-art (pp. 105–122). Retrieved form https://telearn.archives-ouvertes.fr/hal-00190213

  • List, A., Van Meter, P., Lombardi, D., & Kendeou, P. (2020). Loggers and conservationists: Navigating the multiple resource forest through the trees. In P. Van Meter, A. List, D. Lombardi, & P. Kendeou (Eds.), Handbook of learning from multiple representations and perspectives. Routledge is an imprint of the Taylor & Francis Group.

    Google Scholar 

  • Mangen, A., Walgermo, B. R., & Brønnick, K. (2013). Reading linear texts on paper versus computer screen: Effects on reading comprehension. International Journal of Educational Research, 58, 61–68.

    Article  Google Scholar 

  • Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.

    Book  Google Scholar 

  • Mayer, R. E., Cook, L. K., & Dyck, J. L. (1983). Techniques that help readers build mental models from scientific text: Definitions pretraining and signaling. Journal of Educational Psychology, 76(6), 1089–1105.

    Article  Google Scholar 

  • McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14(1), 1–43.

    Article  Google Scholar 

  • Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5, 14–37.

    Article  Google Scholar 

  • Ntshalintshali, G. N., & Clariana, R. B. (2020). Paraphrasing refutation text improved higher knowledge forms and hindered lower knowledge forms: Examples from repairing relational database design misconceptions. Educational Technology Research and Development, 68, 2165–2183. https://doi.org/10.1007/s11423-020-09758-5

    Article  Google Scholar 

  • OECD. (2011). PISA 2009 results: Students on line: digital technologies and performance (Vol. VI). https://doi.org/10.1787/9789264112995-en

  • Peer, M., Brunec, I. K., Newcombe, N. S., & Epstein, R. A. (2021). Structuring knowledge with cognitive maps and cognitive graphs. Trends in Cognitive Sciences, 25, 37–54. https://doi.org/10.1016/j.tics.2020.10.004

    Article  Google Scholar 

  • Pettersson, R. (2012). Introduction to message design. Journal of Visual Literacy, 31(2), 93–104. https://doi.org/10.1080/23796529.2012.11674702

    Article  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.

    Article  Google Scholar 

  • Poindexter, M. T., & Clariana, R. B. (2006). The influence of relational and proposition-specific processing on structural knowledge and traditional learning outcomes. International Journal of Instructional Media, 33(2), 177–184.

    Google Scholar 

  • Quillian, M. (1968). Semantic memory. In M. Minsky (Ed.), Semantic information processing (pp. 227–270). MIT Press.

    Google Scholar 

  • Robinson, D. H., & Kiewra, K. A. (1995). Visual argument: Graphic organizers are superior to outline in improving learning from text. Journal of Educational Psychology, 87(3), 455–467.

    Article  Google Scholar 

  • Rolfes, T., Roth, J., & Schnotz, W. (2018). Effects of tables, bar charts, and graphs on solving function tasks. Journal Für Mathematik-Didaktik, 39(1), 97–125.

    Google Scholar 

  • Rumelhart, D. (1980). Schemata: The building blocks of cognition. In R. Spiro, B. Bruce, & W. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 33–58). Erlbaum Associates.

    Google Scholar 

  • Schnotz, W. (1984). Comparative instructional text organization. In H. Mandl, N. L. Stein, & T. Trabasso (Eds.), Learning and comprehension of text (pp. 53–81). Lawrence Erlbaum.

    Google Scholar 

  • Schnotz, W. (1993). Adaptive construction of mental representations in understanding expository texts. Contemporary Educational Psychology, 18(1), 114–120. https://doi.org/10.1006/ceps.1993.1011

    Article  Google Scholar 

  • Schnotz, W., & Bannert, M. (2003). Construction and interference in learning from multiple representation. Learning and Instruction, 13(2), 141–156.

    Article  Google Scholar 

  • Schvaneveldt, R. W. (2004). Finding meaning in psychology. In A. F. Healy (Ed.), Experimental cognitive psychology and its applications: Festschrift in honor of Lyle Bourne, Walter Kintsch, and Thomas Landauer. American Psychological Association.

    Google Scholar 

  • Schvaneveldt, R. W. (2020). Pathfinder [Computer software]. Retrieved from https://research-collective.com/PFWeb/

  • Schvaneveldt, R. W., Durso, F. T., Goldsmith, T. E., Breen, T. J., Cooke, N. M., Tucker, R. G., & De Maio, J. C. (1985). Measuring the structure of expertise. International Journal of Man-Machine Studies, 23(6), 699–728.

    Article  Google Scholar 

  • Schwonke, R., Berthold, K., & Renkl, A. (2009). How multiple external representations are used and how they can be made more useful. Applied Cognitive Psychology, 23(9), 1227–1243.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Shimojima, A., & Katagiri, Y. (2012). An eye-tracking study of integrative spatial cognition over diagrammatic representations spatial cognition. In C. Hölscher, T. Shipley, M. O. Belardinelli, J. Bateman, & Newcombe N. (Eds.), Spatial cognition vii: International Conference, Spatial Cognition 2010, vol 6222 (pp. 262–278). Berlin, DE: SpringerBerlin.

  • Spector, J. M. (2006). A methodology for assessing learning in complex and ill-structured task domains. Innovations in Education and Teaching International, 43(2), 109–120. https://doi.org/10.1080/14703290600650368

    Article  Google Scholar 

  • Spector, J. M., & Koszalka, T. A. (2004). The DEEP methodology for assessing learning in complex domains. Technical Report No. NSF-03-542. Syracuse, NY: Syracuse University, Instructional Design Development, and Evaluation (IDD&E).

  • Shreiner, T. L., & Dykes, B. M. (2021). Visualizing the teaching of data visualizations in social studies: A study of teachers’ data literacy practices, beliefs, and knowledge. Theory and Research in Social Education, 49(2), 262–306. https://doi.org/10.1080/00933104.2020.1850382

    Article  Google Scholar 

  • Tang, H., & Clariana, R. (2017). Leveraging a sorting task as a measure of knowledge structure in bilingual settings. Technology, Knowledge and Learning, 22, 23–35.

    Article  Google Scholar 

  • Teplovs, C., & Scardamalia, M. (2007). Visualizations for knowledge building assessment. [Conference presentation]. AgileViz workshop, CSCL 2007 Convention, New Brunswick, NJ, United States. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.590.1779

  • van Dijk, T. A., & Kintsch, W. (1983). Strategies of discourse comprehension. Academic Press.

    Google Scholar 

  • Wang, X. (2016). Tabular abstraction, editing, and formatting. UWSpace. Retrieved from http://hdl.handle.net/10012/10962

  • Wright, P. (1980). The comprehension of tabulated information: Some similarities between reading text and reading Tables. NSPI Journal, 19(8), 25–29.

    Article  Google Scholar 

  • Zhang, J., Tao, D., Chen, M.-H., Sun, Y., Judson, D., & Naqvi, S. (2018). Co-organizing the collective journey of inquiry with idea thread mapper. Journal of the Learning Sciences, 27(3), 390–430. https://doi.org/10.1080/10508406.2018.1444992

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Penn State University, 314 Keller building, University Park, PA, 16802, USA

    Minkyung Lee

  2. Penn State Univerisity, 305D Keller Building, University Park, PA, 16802, USA

    Roy B. Clariana

Authors
  1. Minkyung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roy B. Clariana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Minkyung Lee.

Ethics declarations

Conflict of interest

The authors declare that we have no potential conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This article does not contain any studies involving animals performed by any of the authors.

Informed consent

Implied or verbal consent was obtained. Subjects did not sign a consent form (waiver of written documentation of consent). The research presents no more that minimal risk of harm to subjects and involves no procedures for which written consent is normally required outside of the research context.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, M., Clariana, R.B. The influence of external concept structures on an individual’s knowledge structures. Education Tech Research Dev 70, 1657–1674 (2022). https://doi.org/10.1007/s11423-022-10144-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-022-10144-6

Keywords

Search

Navigation