Improving Students’ Argumentation Skills Using Dynamic Machine-Learning–Based Modeling

References

• Abbasi A, Chen H, Salem A (2008) Sentiment analysis in multiple languages. ACM Trans. Inform. Systems 26(3):1–34.Crossref, Google Scholar
• Abbasi A, Zhou Y, Deng S, Zhang P (2018) Text analytics to support sense-making in social media: A language-action perspective. MIS Quart. 42(2):427–464.Crossref, Google Scholar
• Afrin T, Kashefi O, Olshefski C, Litman D, Hwa R, Godley A (2021) Effective interfaces for student-driven revision sessions for argumentative writing. Proc. 2021 CHI Conf. Human Factors Comput. Systems (ACM, New York), 1–13.Google Scholar
• Agarwal R, Karahanna E (2000) Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quart. 24(4):665.Crossref, Google Scholar
• Alavi M, Leidner DE (2001) Research commentary: Technology-mediated learning - A call for greater depth and breadth of research. Inform. Systems Res. 12(1):1–10.Link, Google Scholar
• Anderson K (1986) Computer assisted instruction: An overview. Proc. Hawaii Internat. Conf. System Sci. 3(October):4–10.Google Scholar
• Anderson JR, Corbett AT, Koedinger KR, Ray P (1995) Cognitive tutors: Lessons learned. J. Learn. Sci. 4(2):167–207.Crossref, Google Scholar
• Ashford SJ (1986) Feedback-seeking in individual adaptation: A resource perspective. Acad. Management J. 29(3):465–487.Crossref, Google Scholar
• Bandura A (1977) Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 84(2):191–215.Crossref, Google Scholar
• Bandura A (1986) Social Foundations of Thought and Action: A Social Cognitive Theory (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Bandura A (1991) Social cognitive theory of self-regulation. Organ. Behav. Human Decision Processes 50(2):248–287.Crossref, Google Scholar
• Bandura A (2001) Social cognitive theory: An agentic perspective. Annu. Rev. Psychol. 52:1–26.Crossref, Google Scholar
• Bauman K, Tuzhilin A (2018) Recommending remedial learning materials to students by filling their knowledge gaps. MIS Quart. 42(1):313–332.Crossref, Google Scholar
• Bell BS, Kozlowski SWJ (2008) Active learning: Effects of core training design elements on self-regulatory processes, learning, and adaptability. J. Appl. Psychol. 93(2):296–316.Crossref, Google Scholar
• Bjork RA, Dunlosky J, Kornell N (2013) Self-regulated learning: Beliefs, techniques, and illusions. Ann. Rev. Psychol. 64(November 2012):417–444.Crossref, Google Scholar
• Bostrom RP, Heinen JS (1977) MIS problems and failures: A socio-technical perspective. Part I: The causes. MIS Quart. 1(3):17.Crossref, Google Scholar
• Brinton CG, Rill R, Ha S, Chiang M, Smith R, Ju W (2015) Individualization for education at Scale: MIIC design and preliminary evaluation. IEEE Trans. Learn. Technol. 8(1):136–148.Crossref, Google Scholar
• Brown KG, Howardson G, Fisher SL (2016) Learner control and e-learning: Taking stock and moving forward. Annu. Rev. Organ. Psychol. Organ. Behav. 3:267–291.Crossref, Google Scholar
• Byrne RMJ (1989) Everyday reasoning with conditional sequences. Quart. J. Exp. Psychol. Section A 41(1):141–166.Crossref, Google Scholar
• Caliskan A, Bryson JJ, Narayanan A (2017) Semantics derived automatically from language corpora contain human-like biases. Science 356(6334):183–186.Crossref, Google Scholar
• Carter MJ, Ste-Marie DM (2017) An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules. Psychol. Res. 81(2):399–406.Crossref, Google Scholar
• Chernodub A, Oliynyk O, Heidenreich P, Bondarenko A, Hagen M, Biemann C, Panchenko A (2019) TARGER: Neural argument mining at your fingertips. Proc. 57th Annual Meeting Assoc. Comput. Linguistics: System Demonstrations (Association for Computational Linguistics, Kerrville, TX), 195–200.Google Scholar
• Cohen J (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd ed. (Routledge, New York).Google Scholar
• Compeau DR, Higgins CA (1995) Application of social cognitive theory to training for computer skills. Inf. Syst. Res. 6(2):118–143.Link, Google Scholar
• Daniel B (2015) Big data and analytics in higher education: Opportunities and challenges. Br. J. Ed. Technol. 46(5):904–920.Crossref, Google Scholar
• De Groot R, Drachman R, Hever R, Schwartz B, Hoppe U, Harrer A, De Laat M, Wegerif R, Mclaren BM, Baurens B (2007) Computer supported moderation of E-discussions: The ARGUNAUT approach. Proc. Conf. Comput.-Supported Collaborative Learn. (CSCL 2007) (ACM, New York).Google Scholar
• Deng S, Zhou Y, Zhang P, Abbasi A (2019) Using discussion logic in analyzing online group discussions: A text mining approach. Inform. Management 56(4):536–551.Crossref, Google Scholar
• Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: Pre-training of deep bidirectional transformers for language understanding. Proc. 2019 Conf. North American Chapter Assoc Comput. Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers) (Association for Computational Linguistics, Kerrville, TX).Google Scholar
• Diana N (2018) Leveraging educational technology to improve the quality of civil discourse. Internat. Conf. Artificial Intelligence Ed. (Springer Verlag, Cham, Switzerland), 517–520.Google Scholar
• Dickhaut E, Janson A, Söllner M, Leimeister JM (2023) Lawfulness by design – Development and evaluation of lawful design patterns to consider legal requirements. Eur. J. Inf. Syst., ePub ahead of print March 1, https://doi.org/10.1080/0960085X.2023.2174050.Crossref, Google Scholar
• Dietvorst B, Simmons J, Massey C (2018) Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Sci. 64:1155–1170.Link, Google Scholar
• Dillenbourg P (2002) Over-scripting CSCL: The risks of blending collaborative learning with instructional design. Three Worlds of CSCL. Can We Support CSCL? (Open Universiteit Nederland, Heerlen, Netherlands), 61–91.Google Scholar
• Dillenbourg P, Hong F (2008) The mechanics of CSCL macro scripts. Int. J. Computer Supported Collab. Learn. 3(1):5–23.Crossref, Google Scholar
• Dillenbourg P, Järvelä S, Fischer F (2009) The evolution of research on computer-supported collaborative learning. Balacheff N, Ludvigsen S, de Jong T, Lazonder A, Barnes S, eds. Technology-Enhanced Learning: Principles and Products (Springer Netherlands, Dordrecht, Netherlands), 3–19.Crossref, Google Scholar
• Duschl RA, Osborne J (2002) Supporting and promoting argumentation discourse in science education. Stud. Sci. Ed. 38(1):39–72.Crossref, Google Scholar
• Elshan E, Ebel P, Söllner M, Leimeister JM (2023) Leveraging low code development of smart personal assistants: An integrated design approach with the SPADE method. J. Management Inform. Syst. 40(1):96–129.Crossref, Google Scholar
• Eom SB, Wen HJ, Ashill N (2006) The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sci. J. Innovative Ed. 4(2):215–235.Crossref, Google Scholar
• Fadel C, Bialik M, Trilling B (2015) Four-Dimensional Education: The Competencies Learners Need to Succeed (Center for Curriculum Redesign, Boston).Google Scholar
• Fan YC, Wang TH, Wang KH (2020) Studying the effectiveness of an online argumentation model for improving undergraduate students’ argumentation ability. J. Comput. Assisted Learn. 36(4):526–539.Google Scholar
• Fischer F, Kollar I, Stegmann K, Wecker C (2013) Toward a script theory of guidance in computer-supported collaborative learning. Ed. Psychol. 48(1):56–66.Crossref, Google Scholar
• Flender J, Christmann U, Groeben N (1999) Entwicklung und erste Validierung einer Skala zur Erfassung der passiven argumentativ-rhetorischen Kompetenz. Z. Differ. Diagn. Psychol. 20(4):309–325.Google Scholar
• Flesch R (1943) Marks of readable style; a study in adult education. Teachers College Contributions to Education 897.Google Scholar
• Florou E, Konstantopoulos S, Kukurikos A, Karampiperis P (2013) Argument extraction for supporting public policy formulation. Proc. 7th Workshop Language Tech. Cultural Heritage Soc. Sci. Humanities (Association for Computational Linguistics, Stroudsburg, PA), 49–54.Google Scholar
• Fuegener A, Grahl J, Gupta A, Ketter W (2021) Will humans-in-the-loop become borgs? Merits and pitfalls of working with AI. MIS Quart. 45(3):1527–1556.Crossref, Google Scholar
• Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. MIS Quart. 37(2):337–356.Google Scholar
• Guerraoui C, Reisert P, Inoue N, Mim FS, Naito S, Choi J, Inui K (2023) Teach me how to improve my argumentation skills: A survey on feedback in argumentation. Preprint, submitted July 28, https://arxiv.org/abs/2307.15341.Google Scholar
• Gupta S, Bostrom R (2013) An investigation of the appropriation of technology-mediated training methods incorporating enactive and collaborative learning. Inform. Systems Res. 24(2):454–469.Link, Google Scholar
• Gupta S, Bostrom RP (2009) Technology-mediated learning: A comprehensive theoretical model. J. Assoc. Inform. Systems 10(9):686–714.Google Scholar
• Guo K, Zhong Y, Li D, Chu SKW (2023) Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Comput. Ed. 203(15):104862.Crossref, Google Scholar
• Han J, Kim KH, Rhee W, Cho YH (2021) Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation. Comput. Ed. 163:104041.Crossref, Google Scholar
• Hattie J, Timperley H (2007) The power of feedback. Rev. Ed. Res. 77(1):81–112.Crossref, Google Scholar
• Head A, Lo K, Kang D, Fok R, Skjonsberg S, Weld DS, Hearst MA (2021) Augmenting scientific papers with just-in-time, position-sensitive definitions of terms and symbols. Proc. 2021 CHI Conf. Human Factors Comput. Systems (CHI ’21) (Association for Computing Machinery, New York), 1–18.Google Scholar
• Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. Design Sci. IS Res. MIS Quart. 28(1):75.Google Scholar
• Hone KS, El Said GR (2016) Exploring the factors affecting MOOC retention: A survey study. Comput. Ed. 98:157–168.Crossref, Google Scholar
• Hovy D, Prabhumoye S (2021) Five sources of bias in natural language processing. Lang. Linguist. Compass. 15(8):e12432.Crossref, Google Scholar
• Huang CJ, Chang SC, Chen HM, Tseng JH, Chien SY (2016) A group intelligence-based asynchronous argumentation learning-assistance platform. Interact. Learn. Environ. 24(7):1408–1427.Crossref, Google Scholar
• Huang N, Zhang J, Burtch G, Li X, Chen P (2021) Combating procrastination on massive online open courses via optimal calls to action. Inform. Systems Res. 32(2):301–317.Link, Google Scholar
• Janson A, Söllner M, Leimeister JM (2020) Ladders for learning: Is scaffolding the key to teaching problem-solving in technology-mediated learning contexts? Acad. Management Learn. Ed. 19(4):439–468.Crossref, Google Scholar
• Jonassen DH, Kim B (2010) Arguing to learn and learning to argue: Design justifications and guidelines. Ed. Technol. Res. Dev. 58(4):439–457.Crossref, Google Scholar
• Jung R, Lehrer C (2017) Guidelines for education in business and information systems engineering at tertiary institutions. Bus. Inform. Systems Engrg. 59(3):189–203.Crossref, Google Scholar
• Kalyuga S (2007) Expertise reversal effect and its implications for learner-tailored instruction. Ed. Psychol. Rev. 19(4):509–539.Crossref, Google Scholar
• Kalyuga S, Ayres P, Chandler P, Sweller J (2003) The expertise reversal effect. Ed. Psychol. 38(1):23–31.Crossref, Google Scholar
• Kim D, Benbasat I (2006) The effects of trust-assuring arguments on consumer trust in Internet stores: Application of Toulmin’s model of argumentation. Inform. Systems Res. 17(3):286–300.Link, Google Scholar
• Knoth N, Tolzin A, Janson A, Leimeister JM (2024) AI literacy and its implications for prompt engineering strategies. Comput. Ed. Artificial Intelligence 9(2):100225.Crossref, Google Scholar
• Koschmann T, ed. (1996) CSCL: Theory and Practice of an Emerging Paradigm (Lawrence Erlbaum Associates, Inc., Mahwah, NJ).Google Scholar
• Kuhn D (1992) Thinking as argument. Harv. Ed. Rev. 62(2):155–179.Crossref, Google Scholar
• Kuhn D (1993) Science as argument: Implications for teaching and learning scientific thinking. Sci. Ed. 77(3):319–337.Crossref, Google Scholar
• Kulik JA, Fletcher JD (2016) Effectiveness of intelligent tutoring systems: A meta-analytic review. Rev. Ed. Res. 86(1):42–78.Crossref, Google Scholar
• Kurpicz-Briki M (2020) Cultural differences in bias? Origin and gender bias in pre-trained German and French word embeddings. Proc. 5th Swiss Text Analytics Conf. (SwissText) & 16th Conf. Natl. Language Processing (KONVENS) (Zurich, Switzerland).Google Scholar
• Lauscher A, Glavaš G, Eckert K (2019) ArguminSci: A tool for analyzing argumentation and rhetorical aspects in scientific writing. Proc. 5th Workshop Argument Mining (Association for Computational Linguistics, Stroudsburg, PA), 22–28.Google Scholar
• Lawrence J, Reed C (2019) Argument mining: A survey. Comput. Linguist. 45(4):765–818.Crossref, Google Scholar
• Lederman D (2018) New data: Online enrollments grow, and share of overall enrollment grows faster. Retrieved November 12, https://www.insidehighered.com/digital-learning/article/2018/11/07/new-data-online-enrollments-grow-and-share-overall-enrollment.Google Scholar
• Lehmann T, Hähnlein I, Ifenthaler D (2014) Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Comput. Human Behav. 32:313–323.Crossref, Google Scholar
• Leidner DE, Jarvenpaa SL (1995) The use of information technology to enhance management school education: A theoretical view. MIS Quart. 19(3):265–291.Crossref, Google Scholar
• Leung ACM, Santhanam R, Kwok RCW, Yue WT (2022) Could gamification designs enhance online learning through personalization? Lessons from a field experiment. Inform. Systems Res. 34(1):27–49.Google Scholar
• Lin YR, Fan B, Xie K (2020) The influence of a web-based learning environment on low achievers’ science argumentation. Comput. Ed. 151:103860.Crossref, Google Scholar
• Lin YR, She HC, Yang WT, Huang KY (2015) Online collaborative learning for improving argumentation of student with different levels of science prior knowledge. Proc. IEEE 15th Internat. Conf. Adv. Learn. Tech. Adv. Tech. Supporting Open Access Formal Informal Learn., ICALT 2015 (IEEE, Piscataway, NJ), 214–215.Google Scholar
• Lippi M, Torroni P (2016) MARGOT: A web server for argumentation mining. Expert Syst. Appl. 65:292–303.Crossref, Google Scholar
• Marcus SL, Rips LJ (1979) Conditional reasoning. J. Verbal Learn. Verbal Behav. 18(2):199–223.Crossref, Google Scholar
• Marcus N, Cooper M, Sweller J (1996) Understanding instructions. J. Ed. Psychol. 88(1):49–63.Crossref, Google Scholar
• Matsuda N, Cohen WW, Koedinger KR (2015) Teaching the teacher: Tutoring SimStudent leads to more effective cognitive tutor authoring. Int. J. Artificial Intelligence Ed. 25(1):1–34.Crossref, Google Scholar
• Mayer RE (2014) Cognitive theory of multimedia learning. The Cambridge Handbook of Multimedia Learning, 2nd ed. (Cambridge University Press, Cambridge, UK), 43–71.Crossref, Google Scholar
• McAlister S, Ravenscroft A, Scanlon E (2004) Combining interaction and context design to support collaborative argumentation using a tool for synchronous CMC. J. Comput. Assisted Learn. 20:194–204.Google Scholar
• McLaren BM, Scheuer O, Mikšátko J (2010) Supporting collaborative learning and e-discussions using artificial intelligence techniques. Int. J. Artificial Intelligence Ed. 20(1):1–46.Google Scholar
• Metcalfe J (2017) Learning from errors. Annu. Rev. Psychol. 68:465–489.Crossref, Google Scholar
• Means ML, Voss J F (1996) Who reasons well? Two studies of informal reasoning among children of different grade, ability and knowledge levels. Cogn. Instr. 14:139–179.Crossref, Google Scholar
• Mochales R, Moens MF (2011) Argumentation mining. Artificial Intelligence Law. 19(1):1–22.Crossref, Google Scholar
• Mochales Palau R, Ieven A (2009) Creating an argumentation corpus: Do theories apply to real arguments? A case study on the legal argumentation of the ECHR. Proc. Twelfth Internat. Conf. Artificial Intelligence Law (ICAIL 2009) (ACM, New York), 21–30.Google Scholar
• Mollick ER, Mollick L (2022) New modes of learning enabled by AI chatbots: Three methods and assignments. Preprint, submitted December 23, http://dx.doi.org/10.2139/ssrn.4300783.Google Scholar
• Nievelstein F, van Gog T, van Dijck G, Boshuizen HPA (2013) The worked example and expertise reversal effect in less structured tasks: Learning to reason about legal cases. Contemp. Ed. Psychol. 38(2):118–125.Crossref, Google Scholar
• Niknejad N, Ismail W, Ghani I, Nazari B, Bahari M, Hussin ARBC (2020) Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation. Inform. Systems 91:101491.Crossref, Google Scholar
• Noetel M, Griffith S, Delaney O, Harris NR, Sanders T, Parker P, Cruz B del P, Lonsdale C (2022) Multimedia design for learning: An overview of reviews with meta-meta-analysis. Rev. Educational Res. 92(3):413–454.Google Scholar
• Noroozi O, Dehghanzadeh H, Talaee E (2020) A systematic review on the impacts of game-based learning on argumentation skills. Entertain. Comput. 35:100369.Crossref, Google Scholar
• Noroozi O, Kirschner PA, Biemans HJA, Mulder M (2018) Promoting Argumentation competence: Extending from first- to second-order scaffolding through adaptive fading. Ed. Psych. Rev. 30(1):153–176.Crossref, Google Scholar
• Noroozi O, Banihashem SK, Taghizadeh Kerman N, Parvaneh Akhteh Khaneh M, Babayi M, Ashrafi H, Biemans HJA (2023) Gender differences in students’ argumentative essay writing, peer review performance and uptake in online learning environments. Interact. Learn. Environ. 31(10):6302–6316.Crossref, Google Scholar
• North Whitehead A, Russell B (1910) Principia Mathematica (Cambridge University Press, Cambridge, UK).Google Scholar
• Nussbaum EM (2008) Using argumentation vee diagrams (AVDs) for promoting argument-counterargument integration in reflective writing. J. Ed. Psychol. 100(3), 549–565.Crossref, Google Scholar
• Nussbaum EM, Winsor DL, Aqui YM, Poliquin AM (2007) Putting the pieces together: Online argumentation vee diagrams enhance thinking during discussions. Int. J. Comput. Supported Collab. Learn. 2(4):479–500.Crossref, Google Scholar
• Nye BD (2014) Barriers to ITS adoption: A systematic mapping study. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Springer Verlag, Cham, Switzerland), 583–590.Crossref, Google Scholar
• OECD (2018) The future of education and skills - Education 2030. OECD, Paris.Google Scholar
• Osborne JF, Henderson JB, MacPherson A, Szu E, Wild A, Yao SY (2016) The development and validation of a learning progression for argumentation in science. J. Res. Sci. Teach. 53(6):821–846.Crossref, Google Scholar
• Palau RM, Moens MF (2009) Argumentation mining: The detection, classification and structure of arguments in text. Proc. 12th Internat. Conf. Artificial Intelligence Law (Association for Computing Machinery, New York), 98–107.Google Scholar
• Pinkwart N, Ashley K, Lynch C, Aleven V (2009) Evaluating an intelligent tutoring system for making legal arguments with hypotheticals. Internat. J. Artificial Intelligence Ed. 19(4):401–424.Google Scholar
• Ravenscroft A, McAlister S, Sagar M (2010) Digital dialogue games and InterLoc: A deep learning design for collaborative argumentation on the web. Educational Technologies for Teaching Argumentation Skills (Bentham Science Publishers, Sharjah, U.A.E.), 277–315.Google Scholar
• Roediger HL, Karpicke JD (2006) Test-enhanced learning: Taking memory tests improves long-term retention. Psychol Sci. 17(3):249–255.Google Scholar
• Rosé C, Wang YC, Cui Y, Arguello J, Stegmann K, Weinberger A, Fischer F (2008) Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. Int. J. Comput. Supported Collab. Learn. 3(3):237–271.Crossref, Google Scholar
• Santhanam R, Liu D, Shen WCM (2016) Gamification of technology-mediated training: Not all competitions are the same. Inform. Systems Res. 27(2):453–465.Link, Google Scholar
• Santhanam R, Sasidharan S, Webster J (2008) Using self-regulatory learning to enhance E-learning-based information technology training. Inform. Systems Res. 19(1):26–47.Link, Google Scholar
• Scheuer O (2015) Toward adaptive argumentation learning systems. Thesis, Universität des Saarlandes, Saarbrücken, Germany.Google Scholar
• Scheuer O, Loll F, Pinkwart N, McLaren BM (2010) Computer-supported argumentation: A review of the state of the art. Int. J. Comput. Supported Collab. Learn. 5(1):43–102.Crossref, Google Scholar
• Scheuer O, Mclaren BM, Loll F, Pinkwart N (2012) Automated analysis and feedback techniques to support and teach argumentation: A survey. Ed. Tech. Teaching Argumentation Skills 2:71–124.Crossref, Google Scholar
• Scheuer O, McLaren BM, Weinberger A, Niebuhr S (2014) Promoting critical, elaborative discussions through a collaboration script and argument diagrams. Instr. Sci. 42(2):127–157.Crossref, Google Scholar
• Schmitt A, Wambsganss T, Janson A, Söllner M (2021) Toward a trust reliance paradox? Exploring the gap between perceived trust in and reliance on algorithmic advice. Forty Second Internat. Conf. Inform. Systems (Austin, TX), 1–17.Google Scholar
• Schunk DH (2012) Learning Theories: An Educational Perspective, 6th ed. (Pearson, Boston).Google Scholar
• Seaman JE, Allen IE, Seaman J (2018) Grade increase: Tracking distance education in the United States. Report, Higher Education Reports - Babson Survey Research Group, Babson Park, MA.Google Scholar
• Shehab HM, Nussbaum EM (2015) Cognitive load of critical thinking strategies. Learn. Instr. 35(3):51–61.Crossref, Google Scholar
• Sidorova A, Evangelopoulos N, Valacich JS, Ramakrishnan T (2008) Uncovering the intellectual core of the information systems discipline. MIS Quart. 32(3):467–482.Crossref, Google Scholar
• Skiera B, Yan S, Daxenberger J, Dombois M, Gurevych I (2022) Using information-seeking argument mining to improve service. J. Serv. Res. 25(4):537–548.Crossref, Google Scholar
• Slonim N, Bilu Y, Alzate C, Bar-Haim R, Bogin B, Bonin F, Choshen L, et al. (2021) An autonomous debating system. Nature 591(7850):379–384.Crossref, Google Scholar
• Smolensky P, Fox B, King R, Lewis C (1988) Computer-aided reasoned discourse or, how to argue with a computer. Guindon R, ed. Cognitive Engineering in the Design of Human-Computer Interaction and Expert Systems (Taylor & Francis Group, Amsterdam), 109–162.Google Scholar
• Soloway E, Guzdial M, Hay KE (1994) Learner-centered design: The challenge for HCI in the 21st century. Interactions 1(2):36–48.Crossref, Google Scholar
• Song Y, Heilman M, Klebanov BB, Deane P (2014) Applying argumentation schemes for essay scoring. Proc. First Workshop Argumentation Mining (Association for Computational Linguistics, Stroudsburg, PA).Google Scholar
• Stab C, Gurevych I (2014) Annotating argument components and relations in persuasive essays. Proc. COLING 2014 25th Internat. Conf. Comput. Linguistics Tech. Papers (Dublin City University and Association for Computational Linguistics, Stroudsburg, PA), 1501–1510.Google Scholar
• Stab C, Gurevych I (2017a) Recognizing insufficiently supported arguments in argumentative essays. Lapata M, Blunsom P, Koller A, eds. Proc. 15th Conf. Eur. Chapter Assoc. Computat. Linguistics: Volume 1, Long Papers (Association for Computational Linguistics, Stroudsburg, PA), 980–990.Google Scholar
• Stab C, Gurevych I (2017b) Parsing argumentation structures in persuasive essays. Comput. Linguist. 43(3):619–659.Crossref, Google Scholar
• Stegmann K, Wecker C, Weinberger A, Fischer F (2012) Collaborative argumentation and cognitive elaboration in a computer-supported collaborative learning environment. Instr. Sci. 40(2):297–323.Crossref, Google Scholar
• Sullivan Y, Davis F, Koh C (2022) Executive functions and information systems learning. MIS Quart. 46(2):813–880.Crossref, Google Scholar
• Suthers DD (2003) Representational guidance for collaborative inquiry. Arguing to Learn (Springer, Dordrecht, Netherlands), 27–46.Google Scholar
• Suthers DD, Hundhausen CD (2001) European Perspectives on Computer-Supported Collaborative Learning (University of Maastricht, Maastricht, Netherlands).Google Scholar
• Suthers DD, Hundhausen CD (2003) An experimental study of the effects of representational guidance on collaborative learning processes. J. Learn. Sci. 12(2):183–218.Google Scholar
• Sweller J, van Merrienboer JJG, Paas FGWC (1998) Cognitive architecture and instructional design. Ed. Psychol. Rev. 10(3):251–296.Crossref, Google Scholar
• Tolzin A, Knoth N, Janson A (2024) Worked examples to facilitate the development of prompt engineering skills. 32nd Eur. Conf. Inform. Systems (ECIS) (AIS Electronic Library, Atlanta).Google Scholar
• Topi H (2018) Using competencies for specifying outcome expectations for degree programs in computing: Lessons learned from other disciplines. 2018 SIGED Internat. Conf. Inform. Systems Ed. Res. (AIS Electronic Library, Atlanta).Google Scholar
• Toulmin SE (1984) Introduction to Reasoning (Macmillan, New York).Google Scholar
• Toulmin SE (2003) The Uses of Argument: Updated Edition (Cambridge University Press, Cambridge, UK).Google Scholar
• Tse C, Roof K (2021) Coursera prices IPO at top of range to raise $519 Million - Bloomberg. Retrieved April 27, https://www.bloomberg.com/news/articles/2021-03-31/online-educator-coursera-s-top-of-range-ipo-raises-519-million.Google Scholar
• Tuunanen T, Peffers K (2018) Population targeted requirements acquisition. Eur. J. Inf. Syst. 27(6):686–711.Crossref, Google Scholar
• Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–1131.Crossref, Google Scholar
• van Eemeren FH, Grootendorst R, Henkemans FS, Blair JA, Johnson RH, Krabbe ECW, Plantin C, et al. (1996) Fundamentals of Argumentation Theory: A Handbook of Historical Backgrounds and Contemporary Developments (Lawrence Erlbaum Associates, Inc., Mahwah, NJ).Google Scholar
• van Gog T, Rummel N (2010) Example-based learning: Integrating cognitive and social-cognitive research perspectives. Ed. Psychol. Rev. 22(2):155–174.Crossref, Google Scholar
• Vansteenkiste M, Ryan RM (2013) On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. J. Psychotherapy Integration 23(3):263–280.Google Scholar
• Venable J, Pries-Heje J, Baskerville R (2012) A comprehensive framework for evaluation in design science research. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7286 (Springer, Berlin, Heidelberg), 423–438.Google Scholar
• Venable J, Pries-Heje J, Baskerville R (2016) FEDS: A framework for evaluation in design science research. Eur. J. Inform. Systems 25(1):77–89.Google Scholar
• Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decision Sci. 39(2):273–315.Crossref, Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Quart. 27(3):425–478.Crossref, Google Scholar
• Visser J, Lawrence J, Reed C, Wagemans J, Walton D (2022) Annotating argument schemes. Plantin C, ed. Argumentation Through Languages and Cultures, 1st ed. (Springer, Cham, Switzerland), 101–139.Google Scholar
• von Aufschnaiter C, Erduran S, Osborne J, Simon S (2008) Arguing to learn and learning to argue: Case studies of how students’ argumentation relates to their scientific knowledge. J. Res. Sci. Teach. 45(1):101–131.Crossref, Google Scholar
• vom Brocke J, Maaß W, Buxmann P, Maedche A, Leimeister JM, Pecht G (2018) Future work and enterprise systems. Bus. Inform. Systems Engrg. 60(4):357–366.Crossref, Google Scholar
• Vygotsky LS (1980) Mind in Society: The Development of Higher Psychological Processes (Harvard University Press, Cambridge, MA).Crossref, Google Scholar
• Walton D, Reed C, Macagno F (2008) Argumentation Schemes (Cambridge University Press, Cambridge, MA).Crossref, Google Scholar
• Wang W, Arya D, Novielli N, Cheng J, Guo JLC (2020) ArguLens: Anatomy of community opinions on usability issues using argumentation models. Conf. Human Factors Comput. Systems Proc. (Association for Computing Machinery, New York).Google Scholar
• Wambsganss T, Rietsche R (2019) Toward designing an adaptive argumentation learning tool. 40th Internat. Conf. Inform. Systems, ICIS 2019 (AIS Electronic Library, Atlanta).Google Scholar
• Wambsganss T, Janson A, Leimeister JM (2022b) Enhancing argumentative writing with automated feedback and social comparison nudging. Comput. Ed. 191:104644.Crossref, Google Scholar
• Wambsganss T, Küng T, Matthias S, Leimeister JM (2021) ArgueTutor: An adaptive dialog-based learning system for argumentation skills. Proc. 2021 CHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York).Google Scholar
• Wambsganss T, Swamy V, Rietsche R, Käser T (2022a) Bias at a second glance: A deep dive into bias for German educational peer-review data modeling. Proc. 29th Internat. Conf. Comput. Linguistics (Gyeongju, Republic of Korea), 1344–1356.Google Scholar
• Wambsganss T, Niklaus C, Söllner M, Handschuh S, Leimeister JM (2020b) A corpus for argumentative writing support in German. 28th Internat. Conf. Comput. Linguistics (Coling) (Barcelona, Spain).Google Scholar
• Wambsganss T, Niklaus C, Cetto M, Söllner M, Handschuh S, Leimeister JM (2020a) AL: An adaptive learning support system for argumentation skills. Proc. 2020 CHI Conf. Human Factors Comput Systems (Association for Computing Machinery, New York).Google Scholar
• Weber F, Wambsganss T, Neshaei P, Söllner M (2024) LegalWriter: An intelligent writing support system for structured and persuasive legal case writing for novice law students. Proc. 2024 CHI Conf. Human Factors Comput. Systems (ACM, New York).Google Scholar
• WEF (2018) The future of jobs report 2018. World Economic Forum (September 17), https://www.weforum.org/publications/the-future-of-jobs-report-2018/.Google Scholar
• Weinberger A, Fischer F (2006) A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Comput. Ed. 46(1):71–95.Crossref, Google Scholar
• Weinberger A, Stegmann K, Fischer F (2010) Learning to argue online: Scripted groups surpass individuals (unscripted groups do not). Comput. Human Behav. 26(4):506–515.Crossref, Google Scholar
• Winkler R, Söllner M, Leimeister JM (2021) Enhancing problem-solving skills with smart personal assistant technology. Comput. Ed. 165:104148.Crossref, Google Scholar
• Xu W, Dainoff MJ, Ge L, Gao Z (2021) From human-computer interaction to human-AI interaction: New challenges and opportunities for enabling human-centered AI, 1–73.Google Scholar
• Yi MY, Davis FD (2003) Developing and validating an observational learning model of computer software training and skill acquisition. Inform. Systems Res. 14(2):146–169.Link, Google Scholar
• Zacher H, Frese M (2018) Action regulation theory: Foundations, current knowledge and future directions. The SAGE Handbook of Industrial, Work & Organizational Psychology: Organizational Psychology, vol. 2, 2nd ed. (Sage Reference, Thousand Oaks, CA), 122–144.Crossref, Google Scholar
• Zhang F, Hwa R, Litman DB, Hashemi H (2016) ArgRewrite: A web-based revision assistant for argumentative writings. Proc. 2016 Conf. North Amer. Chapter Assoc. Comput. Linguistics: Demonstrations (Association for Computational Linguistics, Stroudsburg, PA), 37–41.Google Scholar
• Zimmerman BJ, Schunk DH (2001) Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (Routledge, New York).Google Scholar