Authors:
Michael Burkhard
1
;
Sabine Seufert
1
;
Reto Gubelmann
2
;
Christina Niklaus
2
and
Patcharin Panjaburee
3
Affiliations:
1
Institute for Educational Management and Technologies, University of St.Gallen, St. Jakob-Strasse 21, 9000 St.Gallen, Switzerland
;
2
Institute of Computer Science, University of St. Gallen, Rosenbergstrasse 30, 9000 St. Gallen, Switzerland
;
3
Faculty of Education, Khon Kaen University, 123 Thanon Mittraphap, Mueang Khon Kaen, Thailand
Keyword(s):
Argumentation Learning, Academic Writing, Learning Design, Higher Education, Natural Language Processing, Argument Mining.
Abstract:
In academic writing, the competency to argue is important. However, first-year students often have difficulties to construct good arguments. Advances in natural language processing (NLP) have made it possible to better analyze the writing quality of texts. New tools have emerged which can give students individual feedback on their texts and the structure of their arguments. While the use of these argumentation learning support tools can help create better texts, using them in an academic context also carries risks. Learning scenarios are needed that promote argumentation competency using argumentation tools while also making students aware of their limitations. To address this issue, this paper investigates how a learning design with an argumentation learning support tool can be developed to increase the argumentation competency of first-year students. The conjecture-mapping technique was used, to visualize our assumptions and illustrate the developed learning design. As part of a fi
rst design cycle, the learning design was tested with 80 students in seven academic writing classes at the University of St.Gallen in Switzerland. Preliminary findings suggest that the learning design might be helpful to improve the argumentation competency as well as the data-literacy of students (in relation to argumentation tools). However, further research is necessary to confirm or reject our hypotheses.
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