Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments
Journal
Proceedings of the ACM on Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW)
Type
conference paper
Date Issued
2025
Author(s)
Neshaei Seyed Parsa
;
Tolzin, Antonia
;
Berkle, Yvonne
;
Leuchter, Miriam
;
;
;
Research Team
IWI6
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.
Language
English
Keywords
Argumentation Learning
Writing Assistants
Learning from Errors
Natural Language Processing
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
9
Number
2
Start page
1
End page
32
Pages
32
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
JML_1008.pdf
Size
2.58 MB
Format
Adobe PDF
Checksum (MD5)
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