APA
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Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., & Leimeister, J. M. (2020). AL: An Adaptive Learning Support System for Argumentation Skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–14). New York, NY, USA: Association for Computing Machinery.
Chicago/Turabian
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Wambsganss, Thiemo, Christina Niklaus, Matthias Cetto, Matthias Söllner, Siegfried Handschuh, and Jan Marco Leimeister. “AL: An Adaptive Learning Support System for Argumentation Skills.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. New York, NY, USA: Association for Computing Machinery, 2020.
MLA
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Wambsganss, Thiemo, et al. “AL: An Adaptive Learning Support System for Argumentation Skills.” Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, 2020, pp. 1–14.
BibTeX Click to copy
@inbook{wambsganss2020a,
title = {AL: An Adaptive Learning Support System for Argumentation Skills},
year = {2020},
address = {New York, NY, USA},
pages = {1–14},
publisher = {Association for Computing Machinery},
author = {Wambsganss, Thiemo and Niklaus, Christina and Cetto, Matthias and Söllner, Matthias and Handschuh, Siegfried and Leimeister, Jan Marco},
booktitle = {Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems}
}
Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students’ argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.
