Improving Students Argumentation Learning with Adaptive Self-Evaluation Nudging
Journal
Proceedings of the ACM on Human-Computer Interaction (PACMHCI)
ISSN-Digital
2573-0142
Type
journal article
Date Issued
2022-11-11
Author(s)
Research Team
IWI6
Abstract
Recent advantages from computational linguists can be leveraged to nudge students with adaptive self evaluation based on their argumentation skill level. To investigate how individual argumentation self evaluation will help students write more convincing texts, we designed an intelligent argumentation writing support system called ArgumentFeedback based on nudging theory and evaluated it in a series of three qualitative and quaxntitative studies with a total of 83 students. We found that students who received a self-evaluation nudge wrote more convincing texts with a better quality of formal and perceived argumentation compared to the control group. The measured self-efficacy and the technology acceptance provide promising results for embedding adaptive argumentation writing support tools in combination with digital nudging in traditional learning settings to foster self-regulated learning. Our results indicate that the design of nudging-based learning applications for self-regulated learning combined with computational methods for argumentation self-evaluation has a beneficial use to foster better writing skills of students.
Language
English
Keywords
argumentation learning
adaptive learning
digital nudging
HSG Classification
contribution to scientific community
Refereed
Yes
Volume
6
Number
CSCW2
Start page
1
End page
31
Pages
31
Subject(s)
Division(s)
Eprints ID
267870
File(s)![Thumbnail Image]()
Name
JML_897.pdf
Size
4.42 MB
Format
Adobe PDF
Checksum (MD5)
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