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Instructional Science

, Volume 36, Issue 1, pp 53–73 | Cite as

Concept mapping as a follow-up strategy to learning from texts: what characterizes good and poor mappers?

  • Tatjana S. Hilbert
  • Alexander Renkl
Article

Abstract

Concept maps consist of nodes that represent concepts and links that represent relationships between concepts. Various studies have shown that concept mapping fosters meaningful learning. However, little is known about the specific cognitive processes that are responsible for such mapping effects. In a thinking-aloud study, we analyzed the relations between cognitive processes during concept mapping as well as the characteristics of the concept maps that the learners produced and learning outcomes (38 university students). To test whether differences in learning outcome are due to differences in general abilities, verbal and spatial abilities were also assessed. In a cluster-analysis two types of ineffective learners were identified: ‘non-labeling mappers’ and ‘non-planning mappers’. Effective learners, in contrast, showed much effort in planning their mapping process and constructing a coherent concept map. These strategies were more evident in students with prior concept-mapping experience (‘advanced beginners’) than in those who had not used this learning strategy before (‘successful beginners’). Based on the present findings, suggestions for a direct training approach (i.e., strategy training with worked-out examples) and an indirect training approach (i.e., supporting the learners with strategy prompts) were developed.

Keywords

Concept map Mapping Learning strategy Training Learning from texts Thinking aloud 

Notes

Acknowledgements

We thank our student research assistants Annelie Rothe, Sebastian Sommer, and Marco Wittmann for their assistance in the transcription of the thinking-aloud protocols and in the analysis of the data. We also thank Marcia Neff and Aimee Holmes for proofreading.

References

  1. Amer, A. A. (1994). The effect of knowledge-map and underlining training on the reading comprehension of scientific texts. English Specific Purposes, 13, 35–45.CrossRefGoogle Scholar
  2. Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (1999). Intelligenz-Struktur-Test 2000 [Intelligence-Structure-Test 2000]. Göttingen, Germany: Hogrefe.Google Scholar
  3. Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. W. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214.Google Scholar
  4. Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Combining fading with prompting fosters learning. Journal of Educational Psychology, 95, 774–783.CrossRefGoogle Scholar
  5. Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational psychology: A cognitive view. New York: Holt, Rinehart, and Winston.Google Scholar
  6. Berthold, K., Nückles, M., & Renkl, A. (2004). Writing learning protocols: Prompts foster cognitive and metacognitive activities as well as learning outcomes. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional design for effective and enjoyable computer-supported learning (pp. 193–200). Tübingen, Germany: Knowledge Media Research Center.Google Scholar
  7. Britt, M. A., Perfetti, C. A., Sandak, R., & Rouet, J.-F. (1999). Content integration and source separation in learning from multiple texts. In S. R. Goldman, A. C. Graesser, & P. van den Broek (Eds.), Narrative comprehension, causality, and coherence. Essays in honor of Tom Trabasso (pp. 209–233). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  8. 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, 21–33.CrossRefGoogle Scholar
  9. Chang, K.-E., Sung, Y.-T., & Chen, I.-D. (2002). The effect of concept mapping to enhance text comprehension and summarization. Journal of Experimental Education, 71, 5–23.Google Scholar
  10. Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.CrossRefGoogle Scholar
  11. Chularut, P., & DeBacker, T. K. (2004). The influence of concept mapping on achievement, self-regulation, and self-efficacy in students of English as a second language. Contemporary Educational Psychology, 29, 248–263.CrossRefGoogle Scholar
  12. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.CrossRefGoogle Scholar
  13. Dweck, C. S., & Leggett, E. L. (1998). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256–273.CrossRefGoogle Scholar
  14. Ericsson, K. A., & Simon, H. A. (1993). Verbal reports as data. Psychological Review, 87, 215–251.CrossRefGoogle Scholar
  15. Greenhouse, S. W., & Geisser, S. (1959). On methods in the analysis of profile data. Psychometrika, 24, 95–112.CrossRefGoogle Scholar
  16. Hauser, S., Nückles, M., & Renkl, A. (2006). Supporting concept mapping for learning from text. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.), Proceedings of the 7th international conference of the learning sciences (pp. 243–249). Mahwah, NJ: Erlbaum.Google Scholar
  17. Hilbert, T. S., Renkl, A., Kessler, S, & Reiss, K. (2006). Learning from heuristic examples: An approach to foster the acquisition of heuristic skill in mathematics. In G. Clarebout, & J. Elen (Eds.), Avoiding simplicity, confronting complexity. Advances in studying and designing computer-based powerful learning environments (pp. 135–144). Rotterdam: Sense Publishers.Google Scholar
  18. Horton, P. B., McConney, A. A., Gallo, M., & Woods, A. L. (1993). An investigation of the effectiveness of concept mapping as an instructional tool. Science Education, 77, 95–111.CrossRefGoogle Scholar
  19. Hübner, S., Nückles, M., & Renkl, A. (2006). Prompting cognitive and metacognitive processing in writing-to-learn enhances learning outcomes. In R. Sun (Ed.), Proceedings of the 28th annual conference of the cognitive science society (pp. 357–362). Mahwah, NJ: Erlbaum.Google Scholar
  20. Jonassen, D. H., Beissner, K., & Yacci, M. (1993). Structural knowledge: Techniques for representing, conveying, and acquiring structural knowledge. Hillsdale: Lawrence Erlbaum Associates, Inc.Google Scholar
  21. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
  22. Kintsch, W. (1998). Comprehension: A paradigm for cognition. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  23. Lambiotte, J. G., & Dansereau, D. F. (1992). Effects of knowledge maps and prior knowledge on recall of science lecture content. Journal of Experimental Education, 60, 189–201.CrossRefGoogle Scholar
  24. Novak, J. D. (1990). Concept maps and Vee diagrams: Two metacognitive tools to facilitate meaningful learning. Instructional Science, 19, 29–52.CrossRefGoogle Scholar
  25. Novak, J. D. (1995). Concept maps to facilitate teaching and learning. Prospects, 25, 95–111.CrossRefGoogle Scholar
  26. O’Donnell, A. M., & Dansereau, D. F. (2000). Interactive effects of prior knowledge and material format on cooperative teaching. Journal of Experimental Education, 68, 101–118.CrossRefGoogle Scholar
  27. O’Donnell, A. M., Dansereau, D. F., & Hall, R. H. (2002). Knowledge maps as scaffolds for cognitive processing. Educational Psychology Review, 14, 71–86.CrossRefGoogle Scholar
  28. Rafferty, C. D., & Fleschner, L. K. (1993). Concept mapping: A viable alternative to objective and essay exams. Reading, Research, and Instruction, 32, 25–33.Google Scholar
  29. Reader, W., & Hammond, N. (1994). Computer-based tools to support learning from hypertext: Concept mapping tools and beyond. Computers Education, 22, 99–106.CrossRefGoogle Scholar
  30. Renkl, A. (2005). The worked-out-example principle in multimedia learning. In R. Mayer (Ed.), Cambridge handbook of multimedia learning. Cambridge, UK: Cambridge University Press.Google Scholar
  31. Rewey, K. L., Dansereau, D. F., Skaggs, L. P., & Hall, R. H. (1989). Effects of scripted cooperation and knowledge maps on the processing of technical material. Journal of Educational Psychology, 81, 604–609.CrossRefGoogle Scholar
  32. Robinson, D. H., & Kiewra, K. A. (1995). Visual argument: Graphic organizers are superior to outlines in improving learning from text. Journal of Educational Psychology, 87, 455–467.CrossRefGoogle Scholar
  33. Rummel, N., & Spada, H. (2005). Learning to collaborate: An instructional approach to promoting collaborative problem-solving in computer-mediated settings. Journal of the Learning Sciences, 14, 201–241.CrossRefGoogle Scholar
  34. Schwonke, R., Hauser, S., Nückles, M., & Renkl, A. (2006). Enhancing computer-supported writing of learning protocols by adaptive prompts. Computers in Human Behavior, 22, 77–92.CrossRefGoogle Scholar
  35. Weaver, C. A., & Kintsch, W. (1991). Expository text. In R. Barr, M. L. Kamil, P. B. Mosenthal, & P. D. Pearson (Eds.), Handbook of research in teaching (pp. 230–245). New York: Macmillan Publishing Company.Google Scholar
  36. Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In C. M. Wittrock (Ed.), Handbook of research in teaching (pp. 315–327). New York: Macmillan Publishing Company.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of PsychologyUniversity of FreiburgFreiburgGermany

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