Prof. Dr. Christina Niklaus

Assistant Professor in Computer Science with focus on “Databases and Data Engineering”


Curriculum vitae



University of St. Gallen

Institute of Computer Science



Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing


Conference paper


Thiemo Wambsganss, Christina Niklaus
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland, 2022 May, pp. 8748--8760

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APA   Click to copy
Wambsganss, T., & Niklaus, C. (2022). Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 8748–8760). Dublin, Ireland: Association for Computational Linguistics.


Chicago/Turabian   Click to copy
Wambsganss, Thiemo, and Christina Niklaus. “Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing.” In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 8748–8760. Dublin, Ireland: Association for Computational Linguistics, 2022.


MLA   Click to copy
Wambsganss, Thiemo, and Christina Niklaus. “Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, 2022, pp. 8748–60.


BibTeX   Click to copy

@inproceedings{wambsganss2022a,
  title = {Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing},
  year = {2022},
  month = may,
  address = {Dublin, Ireland},
  pages = {8748--8760},
  publisher = {Association for Computational Linguistics},
  author = {Wambsganss, Thiemo and Niklaus, Christina},
  booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month_numeric = {5}
}

Abstract

We introduce an argumentation annotation approach to model the structure of argumentative discourse in student-written business model pitches. Additionally, the annotation scheme captures a series of persuasiveness scores such as the specificity, strength, evidence, and relevance of the pitch and the individual components. Based on this scheme, we annotated a corpus of 200 business model pitches in German. Moreover, we trained predictive models to detect argumentative discourse structures and embedded them in an adaptive writing support system for students that provides them with individual argumentation feedback independent of an instructor, time, and location. We evaluated our tool in a real-world writing exercise and found promising results for the measured self-efficacy and perceived ease-ofuse. Finally, we present our freely available corpus of persuasive business model pitches with 3,207 annotated sentences in German language and our annotation guidelines.


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