research-article

Improving Students Argumentation Learning with Adaptive Self-Evaluation Nudging

Authors:

Thiemo Wambsganss,

Andreas Janson,

Jan Marco LeimeisterAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 520, Pages 1 - 31

https://doi.org/10.1145/3555633

Published: 11 November 2022 Publication History

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 quantitative 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.

References

[1]

Alessandro Acquisti, Saranga Komanduri, Civis Analytics, Pedro Giovanni Leon, Banco De Mexico, Florian Schaub, Yang Wang, Shomir Wilson, ; L F Cranor, N Sadeh, ; Y Wang, Idris Adjerid, Rebecca Balebako, Laura Brandimarte, Lorrie Faith Cranor, Norman Sadeh, and Manya Sleeper. 2017. 44 Nudges for Privacy and Security: Understanding and Assisting Users' Choices Online ACM Reference Format. Comput. Surveys 50, 3 (2017). https://doi.org/10.1145/3054926

Digital Library

[2]

Tazin Afrin, Omid Kashefi, Christopher Olshefski, Diane Litman, Rebecca Hwa, and Amanda Godley. 2021. Effective Interfaces for Student-Driven Revision Sessions for Argumentative Writing. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM, New York, NY, USA, 1--13. https://doi.org/10.1145/3411764.3445683

Digital Library

[3]

Ritu Agarwal and Elena Karahanna. 2000. Time Flies When You're Having Fun: Cognitive Absorption and Beliefs about Information Technology Usage. MIS Quarterly 24, 4 (12 2000), 665. https://doi.org/10.2307/3250951

Digital Library

[4]

S. J. Ashford. 1986. Feedback-Seeking in Individual Adaptation : A Resource Perspective. Academy of Management Journal 29, 3 (9 1986), 465--487. https://doi.org/10.2307/256219

[5]

Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (3 1977), 191--215. https://doi.org/10.1037/0033--295X.84.2.191

[6]

Albert Bandura. 1991. Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes 50, 2 (12 1991), 248--287. https://doi.org/10.1016/0749--5978(91)90022-L

[7]

Torben Barev, Melanie Schwede, and Andreas Janson. 2021. The Dark Side of Privacy Nudging -- An Experimental Study in the Context of a DigitalWork Environment. HICSS 2021 Proceedings (2021). https://doi.org/10.24251/HICSS.2021.500

[8]

Robert A. Bjork, John Dunlosky, and Nate Kornell. 2013. Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology 64, November 2012 (2013), 417--444. https://doi.org/10.1146/annurev-psych-113011--143823

[9]

Christopher G. Brinton, Ruediger Rill, Sangtae Ha, Mung Chiang, Robert Smith, and William Ju. 2015. Individualization for education at Scale: MIIC design and preliminary evaluation. IEEE Transactions on Learning Technologies 8, 1 (3 2015), 136--148. https://doi.org/10.1109/TLT.2014.2370635

Digital Library

[10]

Ruth M. J. Byrne. 1989. Everyday Reasoning with Conditional Sequences. The Quarterly Journal of Experimental Psychology Section A 41, 1 (2 1989), 141--166. https://doi.org/10.1080/14640748908402357

[11]

Elena Cabrio and Serena Villata. 2012. Natural Language Arguments: A Combined Approach. In 20th European Conference on Artificial Intelligence (ECAI 2012). Montpellier, France.

[12]

Cansu Canca. 2020. Operationalizing Ai Ethics Principles *. Commun. ACM 63, 12 (2020), 3--6. https://doi.org/10.1145/3430368

Digital Library

[13]

Ana Caraban, Evangelos Karapanos, Daniel Gonçalves, and Pedro Campos. 2019. 23 Ways to Nudge: A review of technology-mediated nudging in human-computer interaction. In Conference on Human Factors in Computing Systems - Proceedings. 1--15. https://doi.org/10.1145/3290605.3300733

Digital Library

[14]

Ana Caraban, Loukas Konstantinou, and Evangelos Karapanos. 2020. The nudge deck: A design support tool for technology-mediated nudging. In DIS 2020 - Proceedings of the 2020 ACM Designing Interactive Systems Conference. 395--406. https://doi.org/10.1145/3357236.3395485

Digital Library

[15]

Winston Carlile, Nishant Gurrapadi, Zixuan Ke, and Vincent Ng. 2018. Give me more feedback: Annotating argument persuasiveness and related attributes in student essays. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) 1 (2018), 621--631. https://doi.org/10.18653/v1/p18--1058

[16]

Artem Chernodub, Oleksiy Oliynyk, Philipp Heidenreich, Alexander Bondarenko, Matthias Hagen, Chris Biemann, and Alexander Panchenko. 2019. TARGER: Neural Argument Mining at Your Fingertips. (2019), 195--200. https://doi.org/10.18653/v1/p19--3031

[17]

Robert B. Cialdini and Noah J. Goldstein. 2004. Social influence: Compliance and conformity. Annual Review of Psychology 55 (2004), 591--621. https://doi.org/10.1146/annurev.psych.55.090902.142015

[18]

Harris M. Cooper. 1988. Organizing knowledge syntheses: A taxonomy of literature reviews. Knowledge in Society 1, 1 (1988), 104--126. https://doi.org/10.1007/BF03177550

[19]

Peter Daly and Dennis Davy. 2016. Structural, linguistic and rhetorical features of the entrepreneurial pitch: Lessons from Dragons' Den. Journal of Management Development 35, 1 (2 2016), 120--132. https://doi.org/10.1108/JMD-05--2014-0049

[20]

R De Groot, R Drachman, R Hever, B Schwartz, U Hoppe, A Harrer, M De Laat, R Wegerif, B M Mclaren, and B Baurens. 2007. Computer Supported Moderation of E-Discussions: the ARGUNAUT Approach. Technical Report. http://www.argunaut.org

[21]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. (2018). http://arxiv.org/abs/1810.04805

[22]

Nicholas Diana. 2018. Leveraging educational technology to improve the quality of civil discourse. In International Conference on Artificial Intelligence in Education, Vol. 10948 LNAI. Springer Verlag, 517--520. https://doi.org/10.1007/978--3--319--93846--2{_}97

[23]

Rosalind Driver, Paul Newton, and Jonathan Osborne. 2000. Establishing the norms of scientific argumentation in classrooms. Science Education 84, 3 (2000), 287--312. https://doi.org/10.1002/(SICI)1098--237X(200005)84: 3.0.CO;2-A

[24]

Ana Maria Ducasse. 2020. Evidence-based persuasion: A cross-cultural analysis of entrepreneurial pitch in English and Spanish. Journal of International Entrepreneurship 18, 4 (12 2020), 492--510. https://doi.org/10.1007/s10843-020-00278-0

[25]

Richard A. Duschl and Jonathan Osborne. 2002. Supporting and Promoting Argumentation Discourse in Science Education. Studies in Science Education 38, 1 (1 2002), 39--72. https://doi.org/10.1080/03057260208560187

[26]

Frans H van Eemeren, Rob Grootendorst, Ralph H Johnson, Christian Plantin, Charles A Willard, Rob Grootendorst, Ralph H Johnson, Christian Plantin, and Charles A Willard. 1996. Fundamentals of Argumentation Theory. Routledge. https://doi.org/10.4324/9780203811306

[27]

Sean B. Eom, H. Joseph Wen, and Nicholas Ashill. 2006. The Determinants of Students' Perceived Learning Outcomes and Satisfaction in University Online Education: An Empirical Investigation*. Decision Sciences Journal of Innovative Education 4, 2 (7 2006), 215--235. https://doi.org/10.1111/j.1540--4609.2006.00114.x

[28]

Lauren Eskreis-Winkler, Katherine L Milkman, Dena M Gromet, and Angela L Duckworth. 2019. A large-scale field experiment shows giving advice improves academic outcomes for the advisor. Proceedings of the National Academy of Sciences of the United States of America 116, 30 (2019), 14808--14810. https://doi.org/10.1073/pnas.1908779116

[29]

Charles Fadel, Maya Bialik, and Bernie Trilling. 2015. Four-dimensional education : the competencies learners need to succeed. 177 pages.

[30]

José Santiago Fernández-Vázquez and Roberto Carlos Álvarez-Delgado. 2019. The interaction between rational arguments and emotional appeals in the entrepreneurial pitch. International Journal of Entrepreneurial Behaviour and Research 26, 3 (10 2019), 503--520. https://doi.org/10.1108/IJEBR-06--2019-0334

[31]

Frank Fischer, Ingo Kollar, Karsten Stegmann, and Christof Wecker. 2013. Toward a Script Theory of Guidance in Computer-Supported Collaborative Learning. Educational Psychologist 48, 1 (1 2013), 56--66. https://doi.org/10.1080/00461520.2012.748005

[32]

Joseph L. Fleiss. 1971. Measuring nominal scale agreement among many raters. Psychological Bulletin 76, 5 (11 1971), 378--382. https://doi.org/10.1037/h0031619

[33]

R Flesch. 1943. Marks of readable style; a study in adult education. Teachers College Contributions to Education 897 (1943).

[34]

Eirini Florou, Stasinos Konstantopoulos, Antonis Kukurikos, and Pythagoras Karampiperis. 2013. Argument extraction for supporting public policy formulation. In Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. ACL, 49--54.

[35]

María Paz García-Villalba and Patrick Saint-Dizier. 2012. A Framework to Extract Arguments in Opinion Texts. International Journal of Cognitive Informatics and Natural Intelligence 6, 3 (7 2012), 62--87. https://doi.org/10.4018/jcini.2012070104

[36]

Shirley Gregor, Leona Chandra Kruse, and Stefan Seidel. 2020. Research perspectives: The anatomy of a design principle. Journal of the Association for Information Systems 21, 6 (2020), 1622--1652. https://doi.org/10.17705/1jais.00649

[37]

Katrin Hartwig and Christian Reuter. 2021. Nudging users towards better security decisions in password creation using whitebox-based multidimensional visualisations. Behaviour and Information Technology (2021). https://doi.org/10.1080/0144929X.2021.1876167

[38]

John Hattie and Helen Timperley. 2007. The power of feedback., 81--112 pages. https://doi.org/10.3102/003465430298487

[39]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (11 1997), 1735--1780. https://doi.org/10.1162/neco.1997.9.8.1735

Digital Library

[40]

Kate S Hone and Ghada R El Said. 2016. Exploring the factors affecting MOOC retention: A survey study. Computers and Education 98 (2016), 157--168. https://doi.org/10.1016/j.compedu.2016.03.016

Digital Library

[41]

Chenn Jung Huang, Shun Chih Chang, Heng Ming Chen, Jhe Hao Tseng, and Sheng Yuan Chien. 2016. A group intelligence-based asynchronous argumentation learning-assistance platform. Interactive Learning Environments 24, 7 (2016), 1408--1427. https://doi.org/10.1080/10494820.2015.1016533

[42]

David H. Jonassen and Bosung Kim. 2010. Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology Research and Development 58, 4 (2010), 439--457. https://doi.org/10.1007/s11423-009--9143--8

[43]

Zixuan Ke, Hrishikesh Inamdar, Hui Lin, and Vincent Ng. 2019. Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays. (2019), 3994--4004. https://doi.org/10.18653/v1/p19--1390

[44]

Timothy Koschmann. 1996. Paradigm Shifts and Instructional Technology. Technical Report. 1--23 pages. http://opensiuc.lib.siu.edu/meded_books/4

[45]

Klaus Krippendorf. 1980. Content analysis : an introduction to its methodology. https://lib.ugent.be/en/catalog/rug01:000547037

[46]

Klaus Krippendorff. 2004. Measuring the Reliability of Qualitative Text Analysis Data. Departmental Papers (ASC) 38 (2004). https://doi.org/10.1007/s11135-004--8107--7

[47]

Deanna Kuhn. 1992. Thinking as Argument. Harvard Educational Review 62, 2 (7 1992), 155--179. https://doi.org/10. 17763/haer.62.2.9r424r0113t670l1

[48]

Deanna Kuhn. 1993. Science as argument: Implications for teaching and learning scientific thinking. Science Education 77, 3 (6 1993), 319--337. https://doi.org/10.1002/sce.3730770306

[49]

Deanna. Kuhn. 2005. Education for thinking. Harvard University Press. 209 pages. http://www.hup.harvard.edu/ catalog.php?isbn=9780674027459

[50]

John Lawrence and Chris Reed. 2019. Argument mining: A survey. Computational Linguistics 45, 4 (2019), 765--818. https://doi.org/10.1162/COLIa00364

[51]

Doug Lederman. 2018. New data: Online enrollments grow, and share of overall enrollment grows faster., 11-- 12 pages. https://www.insidehighered.com/digital-learning/article/2018/11/07/new-data-online-enrollments-growand- share-overall-enrollment

[52]

Min Kyung Lee, Sara Kiesler, and Jodi Forlizzi. 2011. Mining behavioral economics to design persuasive technology for healthy choices. In Conference on Human Factors in Computing Systems - Proceedings. 325--334. https://doi.org/10.1145/1978942.1978989

Digital Library

[53]

Katja Lehmann, Matthias Söllner, and Jan Marco Leimeister. 2016. Design and Evaluation of an IT-based Peer Assessment to Increase Learner Performance in Large-Scale Lectures. ICIS 2016 Proceedings (12 2016). https://aisel.aisnet.org/icis2016/ISCurriculum/Presentations/8

[54]

Marco Lippi and Paolo Torroni. 2015. Argumentation Mining: State of the Art and Emerging Trends. IJCAI International Joint Conference on Artificial Intelligence 2015-Janua, 2 (2015), 4207--4211. https://doi.org/10.1145/2850417

Digital Library

[55]

Marco Lippi and Paolo Torroni. 2016. MARGOT: A web server for argumentation mining. Expert Systems with Applications 65 (2016), 292--303. https://doi.org/10.1016/j.eswa.2016.08.050

Digital Library

[56]

Sandra L. Marcus and Lance J. Rips. 1979. Conditional reasoning. Journal of Verbal Learning and Verbal Behavior 18, 2 (1979), 199--223. https://doi.org/10.1016/S0022--5371(79)90127-0

[57]

Xia Meng, Zhu Qian, XingboWang, Fei Nie, Huamin Qu, and Ma Xiaojuan. 2022. Persua : A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion. Proceedings of the ACM on Human-Computer Interaction 1, 1 (2022), 1--30. arXiv:4264657 [submit]

[58]

Tobias Mirsch, Christiane Lehrer, and Reinhard Jung. 2017. Digital Nudging: Altering User Behavior in Digital Environments. In Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017).

[59]

Tobias Mirsch, Christiane Lehrer, and Reinhard Jung. 2018. Making digital nudging applicable: The digital nudge design method. In International Conference on Information Systems 2018, ICIS 2018. Association for Information Systems. AIS Electronic Library (AISeL). https://research.cbs.dk/en/publications/making-digital-nudging-applicable-thedigital- nudge-design-method

[60]

Raquel Mochales and Marie-Francine Moens. 2011. Argumentation mining. Artificial Intelligence and Law 19, 1 (3 2011), 1--22. https://doi.org/10.1007/s10506-010--9104-x

[61]

Benjamin Motz, Matthew G Mallon, and Joshua D Quick. 2021. Automated Educative Nudges to Reduce Missed Assignments in College. IEEE Transactions on Learning Technologies (2021). https://doi.org/10.1109/TLT.2021.3064613

[62]

E. Michael Nussbaum, Denise L. Winsor, Yvette M. Aqui, and Anne M. Poliquin. 2007. Putting the pieces together: Online argumentation vee diagrams enhance thinking during discussions. International Journal of Computer-Supported Collaborative Learning 2, 4 (11 2007), 479--500. https://doi.org/10.1007/s11412-007--9025--1

[63]

Stephen D. O'Connell and Guido Lang. 2018. Can Personalized Nudges Improve Learning in Hybrid Classes? Experimental Evidence From an Introductory Undergraduate Course. Journal of Research on Technology in Education 50, 2 (4 2018), 105--119. https://doi.org/10.1080/15391523.2017.1408438

[64]

OECD. 2018. The Future of Education and Skills - Education 2030. https://doi.org/2018-06--15

[65]

OECD. 2019. OECD learning compass 2030: In brief. (2019).

[66]

Jonathan F. Osborne, J. Bryan Henderson, Anna MacPherson, Evan Szu, Andrew Wild, and Shi Ying Yao. 2016. The development and validation of a learning progression for argumentation in science. Journal of Research in Science Teaching 53, 6 (2016), 821--846. https://doi.org/10.1002/tea.21316

[67]

Alexander Osterwalder, Yves Pigneur, Greg Bernarda, and Alan Smith. 2014. Value proposition design : how to create products and services customers want : get started with ... https://books.google.de/books?id=jQ5yBgAAQBAJ&dq=osterwalderbusinessvaluepropositiondesign&hl=de&lr=

[68]

Raquel Mochales Palau and Marie-Francine Moens. 2009. Argumentation Mining: The Detection, Classification and Structure of Arguments in Text. Proceedings of the 12th international conference on artificial intelligence and law (2009), 98--107. https://doi.org/10.1145/1568234.1568246

Digital Library

[69]

Niels Pinkwart, Kevin Ashley, Collin Lynch, and Vincent Aleven. 2009. Evaluating an Intelligent Tutoring System for Making Legal Arguments with Hypotheticals. Technical Report. 401--424 pages. http://iaiedsoc.org/pub/1302/file/19_4_05_Pinkwart.pdf

[70]

George E Raptis and Christina Katsini. 2021. Beter, funner, stronger: A gameful approach to nudge people into making less predictable graphical password choices. In Conference on Human Factors in Computing Systems - Proceedings. ACM, New York, NY, USA, 17. https://doi.org/10.1145/3411764.3445658

Digital Library

[71]

Carolyn Rosé, Yi Chia Wang, Yue Cui, Jaime Arguello, Karsten Stegmann, Armin Weinberger, and Frank Fischer. 2008. Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning 3, 3 (2008), 237--271. https://doi.org/10.1007/s11412-007--9034-0

[72]

Omar Sabaj, Paula Cabezas, Germán Varas, Carlos González-Vergara, and Álvaro Pina-Stranger. 2020. Empirical Literature on the Business Pitch: Classes, Critiques and Future Trends. Journal of technology management {\&} innovation 15, 1 (2020), 55--63. https://doi.org/10.4067/s0718--27242020000100055

[73]

Oliver Scheuer. 2015. Towards adaptive argumentation learning systems. https://www.researchgate.net/publication/298087259

[74]

Oliver Scheuer, Frank Loll, Niels Pinkwart, and Bruce M. McLaren. 2010. Computer-supported argumentation: A review of the state of the art. International Journal of Computer-Supported Collaborative Learning 5, 1 (2010), 43--102. https://doi.org/10.1007/s11412-009--9080-x

[75]

Oliver Scheuer, Bruce M Mclaren, Frank Loll, and Niels Pinkwart. 2012. Automated Analysis and Feedback Techniques to Support and Teach Argumentation: A Survey. Educational Technologies for Teaching Argumentation Skills 2 (2012), 71--124. https://doi.org/10.2174/978160805015411201010071

[76]

Christoph Schneider, Markus Weinmann, and Jan Vom Brocke. 2018. Digital nudging: Guiding online user choices through interface design. Commun. ACM 61, 7 (2018), 67--73. https://doi.org/10.1145/3213765

Digital Library

[77]

Sofia Schöbel, Torben Barev, Andreas Janson, Felix Hupfeld, and Jan Marco Leimeister. 2020. Understanding User Preferences of Digital Privacy Nudges -- A Best-Worst Scaling Approach. HICSS 2020 Proceedings (2020), 3918--3927.

[78]

Julia E. Seaman, I. E. Allen, and Jeff Seaman. 2018. Higher Education Reports - Babson Survey Research Group. Technical Report. http://www.onlinelearningsurvey.com/highered.htmlhttps://www.onlinelearningsurvey.com/highered.html

[79]

Paul Smolensky, Barbara Fox, Roger King, and Clayton Lewis. 1988. Computer-Aided Reasoned Discourse or, How to Argue with a Computer | Taylor & Francis Group. In R. Guindon (Ed.), Cognitive Engineering in the Design of Human-Computer Interaction and Expert Systems. Amsterdam, 109-- 162. https://www.taylorfrancis.com/chapters/computer-aided-reasoned-discourse-argue-computer-paul-smolenskybarbara- fox-roger-king-clayton-lewis/e/10.4324/9780203782040--8

[80]

Elliot Soloway, Mark Guzdial, and Kenneth E Hay. 1994. Learner-Centered Design The Challenge For WC1 In The Xst Century. Interactions (1994), 36--48.

[81]

Yi Song, Michael Heilman, Beata Beigman Klebanov, and Paul Deane. 2014. Applying argumentation schemes for essay scoring. In Proceedings of the First Workshop on Argumentation Mining. 69--78.

[82]

Christian Sonnenberg and Jan vom Brocke. 2012. Evaluation Patterns for Design Science Research Artefacts. Springer, Berlin, Heidelberg, 71--83. https://doi.org/10.1007/978--3--642--33681--2{_}7

[83]

Christian Stab and Iryna Gurevych. 2014. Annotating Argument Components and Relations in Persuasive Essays. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers,. 1501--1510. http://www.ukp.tu-darmstadt.de

[84]

Christian Stab and Iryna Gurevych. 2014. Annotating Argument Components and Relations in Persuasive Essays. In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014). 1501--1510.

[85]

Christian Stab and Iryna Gurevych. 2014. Identifying Argumentative Discourse Structures in Persuasive Essays. In Conference on Empirical Methods in Natural Language Processing (EMNLP 2014)(Oct. 2014), Association for Computational Linguistics, p.(to appear). 46--56. www.ukp.tu-darmstadt.de

[86]

Christian Stab and Iryna Gurevych. 2017. Parsing Argumentation Structures in Persuasive Essays. Computational Linguistics 43, 3 (2017), 619--659. https://doi.org/10.1162/COLI{\{}{\T1\textbackslash}{_}{\}}a{\{}{\T1\textbackslash}{_}{\}}00295

[87]

Christian Stab and Iryna Gurevych. 2017. Recognizing Insufficiently Supported Arguments in Argumentative Essays. Technical Report. 980--990 pages. www.ukp.tu-darmstadt.de

[88]

Karsten Stegmann, Christof Wecker, Armin Weinberger, and Frank Fischer. 2012. Collaborative argumentation and cognitive elaboration in a computer-supported collaborative learning environment. Instructional Science 40, 2 (2012), 297--323. https://doi.org/10.1007/s11251-011--9174--5

[89]

Daniel D Suthers. 2003. Representational Guidance for Collaborative Inquiry. In Arguing to Learn. 27--46. https://doi.org/10.1007/978--94-017-0781--7{\{}{\T1\textbackslash}{_}{\}}2

[90]

Daniel D Suthers and Christopher D Hundhausen. 2001. European Perspectives on Computer-Supported Collaborative Learning. Technical Report. 577--584 pages. http://lilt.ics.hawaii.edu/papers/2001/Suthers-Hundhausen-Euro-CSCL- 2001.pdf

[91]

Richard Thaler and Cass Sunstein. 2009. Nudge: Improving decisions about health, wealth, and happiness.

[92]

Richard H Thaler and Cass R Sunstein. 2009. Nudge: Improving decisions about health, wealth, and happiness. Yale University Press. 1--293 pages. https://doi.org/10.1016/s1477--3880(15)30073--6

[93]

Su Hie Ting. 2018. Ethos, logos and pathos in university students' informal requests. GEMA Online Journal of Language Studies 18, 1 (2018), 234--251. https://doi.org/10.17576/gema-2018--1801--14

[94]

Heikki Topi. 2018. Using competencies for specifying outcome expectations for degree programs in computing: Lessons learned from other disciplines. 2018 SIGED International Conference on Information Systems Education and Research (2018).

[95]

Stephen E. Toulmin. 1984. Introduction to Reasoning.

[96]

Stephen E. Toulmin. 2003. The uses of argument: Updated edition. 1--247 pages. https://doi.org/10.1017/CBO9780511840005

[97]

Crystal Tse and Katie Roof. 2021. Coursera Prices IPO at Top of Range to Raise $519 Million - Bloomberg. https://www.bloomberg.com/news/articles/2021-03--31/online-educator-coursera-s-top-of-range-ipo-raises-519-million

[98]

Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. science 185, 4157 (1974), 1124--1131.

[99]

John Venable, Jan Pries-Heje, and Richard Baskerville. 2016. FEDS: A Framework for Evaluation in Design Science Research. European Journal of Information Systems 25, 1 (2016), 77--89. https://doi.org/10.1057/ejis.2014.36

[100]

Viswanath Venkatesh and Hillol Bala. 2008. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39, 2 (5 2008), 273--315. https://doi.org/10.1111/j.1540--5915.2008.00192.x

[101]

Viswanath Venkatesh, Michael G Morris, Gordon B Davis, and Fred D Davis. 2003. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 3 (2003), 425--478.

[102]

Jan vom Brocke, Wolfgang Maaß, Peter Buxmann, Alexander Maedche, Jan Marco Leimeister, and Günter Pecht. 2018. Future Work and Enterprise Systems. Business and Information Systems Engineering 60, 4 (2018), 357--366. https://doi.org/10.1007/s12599-018-0544--2

[103]

Jan vom Brocke, Alexander Simons, Kai Riemer, Bjoern Niehaves, Ralf Plattfaut, and Anne Cleven. 2015. Standing on the shoulders of giants: Challenges and recommendations of literature search in information systems research. Communications of the Association for Information Systems 37, 1 (8 2015), 205--224. https://doi.org/10.17705/1cais.03709

[104]

Claudia von Aufschnaiter, Sibel Erduran, Jonathan Osborne, and Shirley Simon. 2008. Arguing to learn and learning to argue: Case studies of how students' argumentation relates to their scientific knowledge. Journal of Research in Science Teaching 45, 1 (1 2008), 101--131. https://doi.org/10.1002/tea.20213

[105]

Lev Semenovich Vygotsky. 1980. Mind in society: The development of higher psychological processes. Harvard university press.

[106]

Douglas Walton, Christopher Reed, and Fabrizio Macagno. 2008. Argumentation Schemes. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511802034

[107]

Thiemo Wambsganss, Sebastian Guggisberg, and Matthias Söllner. 2021. ArgueBot: A Conversational Agent for Adaptive Argumentation Feedback. In 16th International Conference on Wirtschaftsinformatik. Essen, Germany.

[108]

Thiemo Wambsganss, Tobias Kueng, Matthias Soellner, and Jan Marco Leimeister. 2021. ArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445781

Digital Library

[109]

Thiemo Wambsganss and Christina Niklaus. 2022. Modeling Persuasive Discourse to Adaptively Support Students' Argumentative Writing. 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - Proceedings of the Conference (2022).

[110]

Thiemo Wambsganss, Christina Niklaus, Matthias Cetto, Matthias Söllner, Jan Marco Leimeister, and Siegfried Handschuh. 2020. AL : An Adaptive Learning Support System for Argumentation Skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1--14.

Digital Library

[111]

Thiemo Wambsganss, Christina Niklaus, Matthias Söllner, Siegfried Handschuh, and Jan Marco Leimeister. 2020. A Corpus for Argumentative Writing Support in German. In 28th International Conference on Computational Linguistics (Coling). Barcelona, Spain. https://doi.org/10.18653/v1/2020.coling-main.74

[112]

Thiemo Wambsganss and Roman Rietsche. 2019. Towards designing an adaptive argumentation learning tool. In 40th International Conference on Information Systems, ICIS 2019. Munich, Germany.

[113]

Wenting Wang, Deeksha Arya, Nicole Novielli, Jinghui Cheng, and Jin L C Guo. 2020. ArguLens: Anatomy of Community Opinions on Usability Issues Using Argumentation Models. Conference on Human Factors in Computing Systems - Proceedings (2020). https://doi.org/10.1145/3313831.3376218

Digital Library

[114]

World Economic Forum WEF. 2018. The Future of Jobs Report 2018. Technical Report. https://doi.org/10.1177/0891242417690604

[115]

Robert J Weijers, Björn B de Koning, and Fred Paas. 2020. Nudging in education: from theory towards guidelines for successful implementation. European Journal of Psychology of Education (2020), 1--20. https://doi.org/10.1007/s10212-020-00495-0

[116]

Armin Weinberger and Frank Fischer. 2006. A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers and Education 46, 1 (2006), 71--95. https://doi.org/10.1016/j.compedu.2005.04.003

[117]

Markus Weinmann, Christoph Schneider, and Jan vom Brocke. 2016. Digital Nudging. Business and Information Systems Engineering 58, 6 (2016), 433--436. https://doi.org/10.1007/s12599-016-0453--1

[118]

Rainer Winkler, Matthias Söllner, and Jan Marco Leimeister. 2021. Enhancing problem-solving skills with smart personal assistant technology. Computers and Education 165 (2021). https://doi.org/10.1016/j.compedu.2021.104148

[119]

Wei Xu, Marvin J Dainoff, Liezhong Ge, and Zaifeng Gao. 2021. From Human-Computer Interaction to Human-AI Interaction: New Challenges and Opportunities for Enabling Human-Centered AI. (2021), 1--73. http://arxiv.org/abs/2105.05424

[120]

Barry J Zimmerman and Dale H Schunk. 2001. Self-regulated learning and academic achievement: Theoretical perspectives. Routledge.

[121]

Verena Zimmermann and Karen Renaud. 2021. The nudge puzzle: Matching nudge interventions to cybersecurity decisions. ACM Transactions on Computer-Human Interaction 28, 1 (2021). https://doi.org/10.1145/3429888

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Cited By

Wan RGebreegziabher SLi TBadillo-Urquiola K(2024)CoCo Matrix: Taxonomy of Cognitive Contributions in Co-writing with Intelligent AgentsProceedings of the 16th Conference on Creativity & Cognition10.1145/3635636.3664260(504-511)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3635636.3664260
Sharma KNguyen AHong Y(2024)Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studiesBritish Journal of Educational Technology10.1111/bjet.13459Online publication date: 9-Apr-2024
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Mirzababaei BPammer-Schindler V(2024)Facilitating the Learning Engineering Process for Educational Conversational Modules Using Transformer-Based Language ModelsIEEE Transactions on Learning Technologies10.1109/TLT.2024.336773817(1222-1235)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3367738
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Improving Students Argumentation Learning with Adaptive Self-Evaluation Nudging

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

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November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

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Published: 11 November 2022

Published in PACMHCI Volume 6, Issue CSCW2

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Wan RGebreegziabher SLi TBadillo-Urquiola K(2024)CoCo Matrix: Taxonomy of Cognitive Contributions in Co-writing with Intelligent AgentsProceedings of the 16th Conference on Creativity & Cognition10.1145/3635636.3664260(504-511)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3635636.3664260
Sharma KNguyen AHong Y(2024)Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studiesBritish Journal of Educational Technology10.1111/bjet.13459Online publication date: 9-Apr-2024
https://doi.org/10.1111/bjet.13459
Mirzababaei BPammer-Schindler V(2024)Facilitating the Learning Engineering Process for Educational Conversational Modules Using Transformer-Based Language ModelsIEEE Transactions on Learning Technologies10.1109/TLT.2024.336773817(1222-1235)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1109/TLT.2024.3367738
Hettler FSchumacher JAnton EEybey BTeuteberg F(2024)Understanding the user perception of digital nudging in platform interface designElectronic Commerce Research10.1007/s10660-024-09825-6Online publication date: 18-Mar-2024
https://doi.org/10.1007/s10660-024-09825-6
Wambsganss TJanson ALeimeister J(2022)Enhancing argumentative writing with automated feedback and social comparison nudgingComputers & Education10.1016/j.compedu.2022.104644191:COnline publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1016/j.compedu.2022.104644

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