Original Research ARTICLE
Learning to argue from others' erroneous arguments - Fostering argumentation competence through learning from advocatory errors
- 1Saarland University, Germany
Argumentation competence is an essential skill to be acquired in university education. However, there is a lack of advanced argumentation competence even for graduate students. To foster argumentation competence, typical interventions focus on example-based learning. Another approach is learning from advocatory errors. The combination of both approaches is presenting examples of erroneous arguments. Drawing on the concept of case-based learning, we developed a learning intervention that presents examples of argumentation errors in story-based designs, i.e., the erroneous examples are embedded in a story featuring the argumentation between two persons in an authentic setting. In this contribution, we report results of two studies. In a first study, we compared an experimental condition receiving a story-based learning intervention with a control condition without a learning intervention. We found that learning from advocatory errors in a story-based design fosters students’ argumentation competence indeed. In a second study, we compared two forms of instructional support (elaboration vs. testing prompts) against a control condition without instructional support. There was a significant increase of argumentation competence in both conditions with instructional support but not in the control condition. The results also support the cautious conclusion that elaboration prompts seem to be more effective than testing prompts. Overall, the results from both studies indicate that the story-based design is apt to foster students’ argumentation competence. We also considered the impact of prior argumentation competence and found in both studies that the present level of argumentation competence is a factor determining the argumentation competence after learning.
Keywords: Argumentation, competence, learning from advocatory errors, Example-based learning, Heuristics
Received: 25 Mar 2020;
Accepted: 26 Jun 2020.
Copyright: © 2020 Klopp and Stark. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Eric Klopp, Saarland University, Saarbrücken, Germany, firstname.lastname@example.org