Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks

Abstract

Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The “text to graph” feature of the software is based on several parsing heuristics and can be used both to assess the learner’s understanding by generating graphical information from his or her text and to generate conceptual graphs from text which can be used as learning materials. In this study we investigate the effects of association nets made available to learners prior to reading. The results reveal that the automatically created graphs are highly similar to classical expert graphs. However, neither the association nets nor the expert graphs had a significant effect on learning, although the latter have been reported to have an effect in previous studies.

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References

  1. Abrams, M. H. (1993). A glossary of literary terms. Fort Worth, TX: Harcourt Brace College Publishers.

    Google Scholar 

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

    Google Scholar 

  3. Brill, E. (1995). Unsupervised learning of dismabiguation rules for part of speech tagging. Paper presented at the Second Workshop on Very Large Corpora, WVLC-95, Boston.

  4. Cañas, A. J., Hill, R., Carff, R., Suri, N., Lott, J., Eskridge, T., et al. (2004). CmapTools: A knowledge modeling and sharing environment. 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. 125–133). Pamplona: Universidad Pública de Navarra.

  5. Christmann, U., & Groeben, N. (1999). Psychologie des Lesens. In B. Franzmann, K. Hasemann, D. Löffler, & E. Schön (Eds.), Handbuch lesen (pp. 145–223). München: Saur.

    Google Scholar 

  6. Crinon, J., & Legros, D. (2002). The semantic effects of consulting a textual database on rewriting. Learning and Instruction, 12(6), 605–626.

    Article  Google Scholar 

  7. Dinter, F. R. (1993). Mentale Modelle als Konstrukt der empirischen Erziehungswissenschaft. [Mental models as constructs of the empirical learning science]. Saarbrücken: Universität Saarbrücken.

  8. Eliaa, I., Gagatsisa, A., & Demetriou, A. (2007). The effects of different modes of representation on the solution of one-step additive problems. Learning and Instruction, 17(6), 658–672.

    Article  Google Scholar 

  9. Groeben, N. (1992). Leserpsychologie: Textverständnis—textverständlichkeit. Münster: Aschendorff.

    Google Scholar 

  10. Hardy, I., & Stadelhofer, B. (2006). Concept Maps wirkungsvoll als Strukturierungshilfen einsetzen. Welche Rolle spielt die Selbstkonstruktion? Zeitschrift für Pädagogische Psychologie, 20(3), 175–187.

    Article  Google Scholar 

  11. 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 

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

    Google Scholar 

  13. Ifenthaler, D. (2010a). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.

    Article  Google Scholar 

  14. Ifenthaler, D. (2010b). 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. New York: Springer.

    Google Scholar 

  15. 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 

  16. Jensen, F. V. (2001). Bayesian networks and decision graphs. New York: Springer.

    Google Scholar 

  17. 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 (Vol. 1, pp. 87–94). San José: Universidad de Costa Rica.

  18. Johnson, T. E., Ifenthaler, D., Pirnay-Dummer, P., & Spector, J. M. (2009). Using concept maps to assess individuals and team in collaborative learning environments. In P. L. Torres & R. C. V. Marriott (Eds.), Handbook of research on collaborative learning using concept mapping (pp. 358–381). Hershey, PA: Information Science Publishing.

    Google Scholar 

  19. 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.

    Google Scholar 

  20. Jonassen, D. H., Reeves, T. C., Hong, N., Harvey, D., & Peters, K. (1997). Concept mapping as cognitive learning and assessment tools. Journal of Interactive Learning Research, 8(3/4), 289–308.

    Google Scholar 

  21. Langer, I., Schulz, V., Thun, F., & Tausch, R. (1974). Verständlichkeit in der Schule, Verwaltung, Politik und Wissenschaft. München: Reinhardt.

    Google Scholar 

  22. Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59(1), 43–64.

    Google Scholar 

  23. 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 

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

    Google Scholar 

  25. 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.

  26. Pirnay-Dummer, P. (2010). Complete structure comparison. In D. Ifenthaler, P. Pirnay-Dummer, & N. M. Seel (Eds.), Computer-based diagnostics and systematic analysis of knowledge. New York: Springer.

    Google Scholar 

  27. Pirnay-Dummer, P., & Rohde, J. (2009). How knowledge assessment methods may be utilized to help learners with their assignments. Using automated assessment tools to provide coaching support during writing tasks. Paper presented at the AERA Annual Meeting, San Diego, CA, USA.

  28. Pirnay-Dummer, P., & Spector, J. M. (2008). Language, association, and model re-representation. How features of language and human association can be utilized for automated knowledge assessment. Paper presented at the AREA 2008, New York.

  29. Pirnay-Dummer, P., & Walter, S. (2009). Bridging the world’s knowledge to individual knowledge using latent semantic analysis and web ontologies to complement classical and new knowledge assessment technologies. Technology, Instruction, Cognition and Learning, 7(1), 21–45.

    Google Scholar 

  30. Pirnay-Dummer, P., Ifenthaler, D., & Rohde, J. (2009). Text-guided automated self-assessment. In D. Kinshuk, G. Sampson, J. M. Spector, P. Isaias & D. Ifenthaler (Eds.), Proceedings of the IADIS international conference on cognition and exploratory learning in the digital age (pp. 311–316). Rome: IADIS Press.

  31. 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 

  32. Pollio, H. R. (1966). The structural basis of word association behavior. The Hague: Mouton.

    Google Scholar 

  33. 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 

  34. Schlomske, N., & Pirnay-Dummer, P. (2009). Model based assessment of learning dependent change within a two semester class. Educational Technology Research and Development, 57(6), 753–765. doi:10.1007/s11423-009-9125-x.

    Article  Google Scholar 

  35. Schnotz, W. (2001). Kognitive Prozesse bei der sprach- und bildgestützten Konstruktion mentaler Modelle. In L. Sichelschmidt & H. Strohner (Eds.), Sprache, Sinn und Situation (pp. 43–57). Wiesbaden: Deutscher Universitätsverlag.

    Google Scholar 

  36. Seel, N. M. (1991). Weltwissen und Mentale Modelle [World knowledge and mental models]. Göttingen: Hogrefe.

  37. Seel, N. M. (2003). Model centered learning and instruction. Technology, Instruction, Cognition and Learning, 1(1), 59–85.

    Google Scholar 

  38. Seel, N. M., & Dinter, F. R. (1995). Instruction and mental model progression: Learner-dependent effects of teaching strategies on knowledge acquisition and analogical transfer. Educational Research and Evaluation, 1(1), 4–35.

    Article  Google Scholar 

  39. Seel, N. M., & Schenk, K. (2003). Multimedia environments as cognitive tools for enhancing model-based learning and problem solving. An evaluation report. Evaluation and Program Planning, 26(2), 215–224.

    Article  Google Scholar 

  40. Smith, W. G. (1894). Mediate association. Mind, 3(11), 289–304.

    Article  Google Scholar 

  41. Smith, W. G. (1918). Methods for studying controlled word associations. Psychobiology, 1(6), 369–428.

    Article  Google Scholar 

  42. Sowa, J. F. (1984). Conceptual structures: Information processing in mind and machine. Reading, MA: Addison-Wesley.

    Google Scholar 

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

    Article  Google Scholar 

  44. Wells, F. L. (1911). Some properties of the free association time. Psychological Review, 18, 1–24.

    Article  Google Scholar 

  45. Williams, F. (1968). Reasoning with statistics. New York: Holt, Rinehart and Winston.

    Google Scholar 

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Correspondence to Pablo Pirnay-Dummer.

Appendix A: Analysis of variance tables

Appendix A: Analysis of variance tables

The influence of the text on the text ratings (Tables 10, 11, 12, 13).

Table 10 ANOVA, simplicity
Table 11 ANOVA, length
Table 12 ANOVA, order/design
Table 13 ANOVA, motivation/stimulation

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Pirnay-Dummer, P., Ifenthaler, D. Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks. Instr Sci 39, 901–919 (2011). https://doi.org/10.1007/s11251-010-9153-2

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Keywords

  • Reading comprehension
  • Mental model
  • Concept map
  • Technology