Concept mapping as a follow-up strategy to learning from texts: what characterizes good and poor mappers?
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.
KeywordsConcept map Mapping Learning strategy Training Learning from texts Thinking aloud
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.
- Amthauer, R., Brocke, B., Liepmann, D., & Beauducel, A. (1999). Intelligenz-Struktur-Test 2000 [Intelligence-Structure-Test 2000]. Göttingen, Germany: Hogrefe.Google Scholar
- 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
- Ausubel, D. P., Novak, J. D., & Hanesian, H. (1978). Educational psychology: A cognitive view. New York: Holt, Rinehart, and Winston.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Kintsch, W. (1998). Comprehension: A paradigm for cognition. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
- 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
- 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
- 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
- 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