[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119068-en":3,"doc-seo-119068-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119068,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling","Argumentation skills underpin persuasion, negotiation, strategic decision making, and constructive discourse, yet learners often struggle because individualized, real-time feedback is scarce and manual feedback is difficult to scale. Grounded in social cognitive theory, the study examines whether dynamic technology-mediated argumentation modeling strengthens students’ argumentation skills in both the short and long term. A dynamic machine-learning–based modeling system delivers writing feedback focused on logical argumentation errors independent of instructor, time, or location. Three empirical studies evaluate persuasive writing improvements versus scripted and adaptive benchmarks.","source: [https://doi.org/10.24451/arbor.22079 | downloaded:](https://doi.org/10.24451/arbor.22079 | downloaded:) 27.9.2024  \n\n| This article was downloaded by: [147 .87.204.44] On: 06 August 2024 , At: 07:39 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA |  |\n| --- | --- |\n|  | Information Systems Research\u003Cbr>Publication details, including instructions for authors and subscription information:\u003Cbr>[http://pubsonline.informs.org](http://pubsonline.informs.org)\u003Cbr>Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling\u003Cbr>Thiemo Wambsganss, Andreas Janson, Matthias Söllner, Ken Koedinger, Jan Marco Leimeister |\n| To cite this article:\u003Cbr>Thiemo Wambsganss, Andreas Janson, Matthias Söllner, Ken Koedinger, Jan Marco Leimeister (2024) Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling. Information Systems Research\u003Cbr>Published online in Articles in Advance 10 Jun 2024\u003Cbr>. [https://doi.org/10.1287/isre.2021.0615](https://doi.org/10.1287/isre.2021.0615)\u003Cbr>Full terms and conditions of use: [https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine](https://pubsonline.informs.org/Publications/Librarians-Portal/PubsOnLine)Terms-and-Conditions\u003Cbr>This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact [permissions@informs.org](permissions@informs.org).\u003Cbr>The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service.\u003Cbr>Copyright © 2024 The Author(s)\u003Cbr>Please scroll down for article—it is on subsequent pages\u003Cbr>With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes.\u003Cbr>For more information on INFORMS, its publications, membership, or meetings visit [http://www.informs.org](http://www.informs.org)\u003Cbr> |  |\n\nDownloaded from informs .org by [ [147.87.204.44](147.87.204.44)] on 06 August 2024, at 07:39 . For personal use only, all rights reserved.  \nINFORMATION SYSTEMS RESEARCH Articles in Advance, pp. 1–34  \n[https://pubsonline.informs.org/journal/isre](https://pubsonline.informs.org/journal/isre) ISSN 1047-7047 (print), ISSN 1526-5536 (online)  \nImproving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling  \nThiemo Wambsganss,a,* Andreas Janson,b Matthias S¨ollner,c Ken Koedinger,d Jan Marco Leimeisterb,e  \na Institute Digital Technology Management, Bern University of Applied Sciences, 3005 Bern, Switzerland; b Institute of Information Systems and Digital Business, University of St. Gallen, 9000 St. Gallen, Switzerland; c Research Center for IS Design, Information Systems and Systems Engineering, University of Kassel, 34121 Kassel, Germany; d Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213; e Research Center for IS Design, Information Systems, University of Kassel, 34121 Kassel, Germany  \n*Corresponding author  \nContact: [thiemo.wambsganss@bfh.ch](thiemo.wambsganss@bfh.ch), https:/orcid.org/0000-0002-7440-9357 (TW); andreas.janson@unisg.ch,  \n[https://orcid.org/0000-0003-3149-0340 (AJ); soellner@uni-kassel.de,","cbCaiuk0Mzx8vZyS","https://ap.wps.com/l/cbCaiuk0Mzx8vZyS","pdf",5965910,1,35,"English","en",105,"# Abstract\n## Dynamic technology-mediated argumentation modeling\n## Machine-learning–based writing feedback\n## Empirical evaluation and benchmarks","[{\"question\":\"Why is improving argumentation skills challenging for learners?\",\"answer\":\"Learners often lack individual and instant feedback, and providing feedback on individual argumentation is time-consuming and not scalable when done manually.\"},{\"question\":\"What is the core solution proposed in the study?\",\"answer\":\"The study builds a dynamic machine-learning–based modeling system that offers writing feedback based on logical argumentation errors.\"},{\"question\":\"How is the approach evaluated?\",\"answer\":\"Three empirical studies test whether dynamic modeling improves persuasive writing performance compared with scripted argumentation modeling and adaptive support.\"}]","Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling | 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