%0 Conference Paper %1 ls_leimeister %A Neshaei, Seyed Parsa %A Tolzin, Antonia %A Berkle, Yvonne %A Leuchter, Miriam %A Leimeister, Jan Marco %A Janson, Andreas %A Wambsganss, Thiemo %B Proceedings of the ACM on Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW) %D 2025 %I Association for Computing Machinery %K argumentation_learning itegpub learning_from_errors natural_language_processing pub_aja pub_ato pub_jml u3bpub writing_assistants %R 10.1145/3711023 %T Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments %U http://pubs.wi-kassel.de/wp-content/uploads/2025/02/JML_1008.pdf %X 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.

@inproceedings{ls_leimeister, 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.}, added-at = {2025-02-05T12:56:40.000+0100}, author = {Neshaei, Seyed Parsa and Tolzin, Antonia and Berkle, Yvonne and Leuchter, Miriam and Leimeister, Jan Marco and Janson, Andreas and Wambsganss, Thiemo}, biburl = {https://www.bibsonomy.org/bibtex/264194da2ac94d30d0218f7383ab140cb/ls_leimeister}, booktitle = {Proceedings of the ACM on Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW)}, doi = {10.1145/3711023}, interhash = {f1e9886245c410b907b347caefd2f1ac}, intrahash = {64194da2ac94d30d0218f7383ab140cb}, keywords = {argumentation_learning itegpub learning_from_errors natural_language_processing pub_aja pub_ato pub_jml u3bpub writing_assistants}, publisher = {Association for Computing Machinery}, timestamp = {2025-02-05T13:52:36.000+0100}, title = {Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments}, url = {http://pubs.wi-kassel.de/wp-content/uploads/2025/02/JML_1008.pdf}, year = 2025 }