Journal article

Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments




Publication Details
Authors:
Neshaei, S.; Tolzin, A.; Berkle, Y.; Leuchter, M.; Leimeister, J.; Janson, A.; Wambsganss, T.

Publication year:
2025
Journal:
Proceedings of the ACM on Human-Computer Interaction
Pages range :
1-32
Journal acronym:
PACMHCI
Volume number:
9
Issue number:
2
ISSN:
2573-0142
eISSN:
2573-0142
DOI-Link der Erstveröffentlichung:
Languages:
English


Abstract

Providing argumentation feedback is considered helpful for students preparing to work in collaborative environments, helping them with writing higher-quality argumentative texts. Domain-independent natural language processing (NLP) methods, such as generative models, can utilize learner errors and fallacies in argumentation learning to help students write better argumentative texts. To test this, we collect design requirements, and then design and implement two different versions of our system called ALure to improve
the students’ argumentation skills. We test how ALure helps students learn argumentation in a university lecture with 305 students and compare the learning gains of the two versions of ALure with a control group using video tutoring. We find and discuss the differences of learning gains in argument structure and fallacies
in both groups after using ALure, as well as the control group. Our results shed light on the applicability of computer-supported systems using recent advances in NLP to help students in learning argumentation as a necessary skill for collaborative working settings.



Keywords
argumentation learning, learning from errors, natural language processing, writing assistants

Last updated on 2025-17-07 at 23:30