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.
ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills
Thiemo Wambsganss,Tobias Kueng,Matthias Söllner,J. Leimeister
Published 2021 in International Conference on Human Factors in Computing Systems
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- Publication year
2021
- Venue
International Conference on Human Factors in Computing Systems
- Publication date
2021-05-06
- Fields of study
Computer Science, Education
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