ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills

Item Type Journal paper
Abstract

Techniques from Natural-Language-Processing offer the opportunities to design new dialog-based forms of human-computer interaction as well as to analyze the argumentation quality of texts. This can be leveraged to provide students with adaptive tutoring when doing a persuasive writing exercise. To test if individual tutoring for students' argumentation will help them to write more convincing texts, we developed ArgueTutor, a conversational agent that tutors students with adaptive argumentation feedback in their learning journey. We compared ArgueTutor with 55 students to a traditional writing tool. We found students using ArgueTutor wrote more convincing texts with a better quality of argumentation compared to the ones using the alternative approach. The measured level of enjoyment and ease of use provides promising results to use our tool in traditional learning settings. Our results indicate that dialog-based learning applications combined with NLP text feedback have a beneficial use to foster better writing skills of students.

Authors Wambsganss, Thiemo; Küng, Tobias; Matthias, Söllner & Leimeister, Jan Marco
Language English
Subjects computer science
information management
education
HSG Classification contribution to scientific community
HSG Profile Area SoM - Business Innovation
Refereed Yes
Date April 2021
Publisher ACM CHI Conference on Human Factors in Computing Systems
Place of Publication Yokohama, Japan
Publisher DOI https://doi.org/10.1145/3411764.3445781
Depositing User Thiemo Wambsganss
Date Deposited 31 Jan 2021 21:07
Last Modified 31 Jan 2021 21:07
URI: https://www.alexandria.unisg.ch/publications/262207

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Citation

Wambsganss, Thiemo; Küng, Tobias; Matthias, Söllner & Leimeister, Jan Marco (2021) ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills.

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https://www.alexandria.unisg.ch/id/eprint/262207
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