Effects of a training intervention to foster argumentation skills while processing conflicting scientific positions
Argumentation skills play a crucial role in science education and in preparing school students to act as informed citizens. While processing conflicting scientific positions regarding topics such as sustainable development in the domain of ecology, argumentation skills such as evaluating arguments or supporting theories with evidence are beneficial for developing deep understanding and well-grounded conclusions. We developed a 50-min training intervention to foster argumentation skills in the domain of ecology on topics related to sustainable development and analyzed its effects in a control-group design: (a) training intervention to foster argumentation skills (n = 41), (b) no such training intervention (n = 42). Results showed that this short-term training intervention successfully fostered three components of argumentation skills (i.e., evaluative knowledge, generative knowledge, and argument quality) and declarative knowledge about argumentation. The positive effect on declarative knowledge was stable 1 week after the training and it was mediated by learning processes during the training intervention: self-explaining the principles of argumentation underlying the video-based examples mediated the effect on declarative knowledge 1 week after the training. In short, the training intervention is an effective instructional method to enhance argumentation skills as well as declarative knowledge about argumentation.
KeywordsArgumentation Training intervention Self-explanations Mediation Conflicting scientific positions Sustainable development
The research in the article was funded by the “Deutsche Forschungsgemeinschaft [German Research Foundation]” (DFG, GZ: BE 4391/1-1) as part of the Special Priority Program “Science and the General Public: Understanding Fragile and Conflicting Scientific Evidence” (Spokesperson of the program: Prof. Rainer Bromme). We would like to thank the following student research assistants: Lea Marie Stieghorst for her acting performance in a video-based example, her assistance in conducting the experiment and in coding the qualitative data, Jörn Weitz for his assistance in conducting the experiment and in coding the qualitative data, Christoph Carstens and Anna Mittelstädt for their acting performance in the video-based examples and their assistance in conducting the experiment, Dominik Bruhn, Manuel Hellmann, and Alexander Tombrink for their acting performance in the video-based examples, Nadine Ellerbrake for her assistance in coding the qualitative data, Matthias Sandmann for his assistance in conducting the experiment, Florian Kopp for his assistance in programming the computer-based environment, and native speaker of English Stewart Campbell for proofreading the manuscript. Finally, we would like to thank all students who took part in our experiment as well as all teachers, the schools’ personnel, and all of those involved in establishing contact to the schools for their support.
- Busch, C., Renkl, A., & Schworm, S. (2008). Towards a generic self-explanation training intervention for example-based learning. In G. Kanselaar, V. Jonker, P. A. Kirschner & F. J. Prins (Eds.), Proceedings of the 8th International Conference of the Learning Sciences. Utrecht, Netherlands: ICLS.Google Scholar
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
- Ellenberg, H. (2009). Vegetation ecology of central Europe. Cambridge, MA: Cambridge University Press.Google Scholar
- Erduran, S., & Jiménez-Aleixandre, M. P. (2008). Argumentation in science education. The Netherlands: Springer.Google Scholar
- Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: Guilford Press.Google Scholar
- Huck, S. W. (2008). Statistical Misconceptions. New York: Taylor & Francis.Google Scholar
- Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. doi: 10.1207/s15326985ep4102_1.CrossRefGoogle Scholar
- Kuhn, D. (2005). Education for thinking. Cambridge, MA: Harvard University Press.Google Scholar
- Quinn, H., Schweingruber, H., & Keller, T. E. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.Google Scholar
- Renkl, A. (2011). Instruction based on examples. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 272–295). New York: Routledge.Google Scholar
- Sobel, M. E. (1982). Aysmptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed.), Sociological methodology (pp. 290–312). San Francisco: Jossey-Bass.Google Scholar