Argumentation Stance Polarity and Intensity Prediction and its Application for Argumentation Polarization Modeling and Diverse Social Connection Recommendation

Cyber argumentation platforms implement theoretical argumentation structures that promote higher quality argumentation and allow for informative analysis of the discussions. Dr. Liu’s research group has designed and implemented a unique platform called the Intelligent Cyber Argumentation System (ICAS). ICAS structures its discussions into a weighted cyber argumentation graph, which describes the relationships between the different users, their posts in a discussion, the discussion topic, and the various subtopics in a discussion. This platform is unique as it encodes online discussions into weighted cyber argumentation graphs based on the user’s stances toward one another’s arguments and ideas. The resulting weighted cyber argumentation graphs can then be used by various analytical models to measure aspects of the discussion. In prior work, many aspects of cyber argumentation have been modeled and analyzed using these stance relationships. However, many challenging problems remain in cyber argumentation. In this dissertation, I address three of these problems: 1) modeling and measure argumentation polarization in cyber argumentation discussions, 2) encouraging diverse social networks and preventing echo chambers by injecting ideological diversity into social connection recommendations, and 3) developing a predictive model to predict the stance polarity and intensity relationships between posts in online discussions, allowing discussions from outside of the ICAS platform to be encoded as weighted cyber argumentation graphs and be analyzed by the cyber argumentation models. In this dissertation, I present models to measure polarization in online argumentation discussions, prevent polarizing echo-chambers and diversifying users’ social networks ideologically, and allow online discussions from outside of the ICAS environment to be analyzed using the previous models from this dissertation and the prior work from various researchers on the ICAS system. This work serves to progress the field of cyber argumentation by introducing a new analytical model for measuring argumentation polarization and developing a novel method of encouraging ideological diversity into social connection recommendations. The argumentation polarization model is the first of its kind to look specifically at the polarization among the users contained within a single discussion in cyber argumentation. Likewise, the diversity enhanced social connection recommendation re-ranking method is also the first of its kind to introduce ideological diversity into social connections. The former model will allow stakeholders and moderators to monitor and respond to argumentation polarization detected in online discussions in cyber argumentation. The latter method will help prevent network-level social polarization by encouraging social connections among users who differ in terms of ideological beliefs. This work also serves as an initial step to expanding cyber argumentation research into the broader online deliberation field. The stance polarity and intensity prediction model presented in this dissertation is the first step in allowing discussions from various online platforms to be encoded into weighted cyber argumentation graphs by predicting the stance weights between users’ posts. These resulting predicted weighted cyber augmentation graphs could then be used to apply cyber argumentation models and methods to these online discussions from popular online discussion platforms, such as Twitter and Reddit, opening many new possibilities for cyber argumentation research in the future

Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development. Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged. In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform. These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions

Polarization and opinion analysis in an online argumentation system for collaborative decision support

Argumentation is an important process in a collaborative decision making environment. Argumentation from a large number of stakeholders often produces a large argumentation tree. It is challenging to comprehend such an argumentation tree without intelligent analysis tools. Also, limited decision support is provided for its analysis by the existing argumentation systems. In an argumentation process, stakeholders tend to polarize on their opinions, and form polarization groups. Each group is usually led by a group leader. Polarization groups often overlap and a stakeholder is a member of multiple polarization groups. Identifying polarization groups and quantifying a stakeholder\u27s degree of membership in multiple polarization groups helps the decision maker understand both the social dynamics and the post-decision effects on each group. Frameworks are developed in this dissertation to identify both polarization groups and quantify a stakeholder\u27s degree of membership in multiple polarization groups. These tasks are performed by quantifying opinions of stakeholders using argumentation reduction fuzzy inference system and further clustering opinions based on K-means and Fuzzy c-means algorithms. Assessing the collective opinion of the group on individual arguments is also important. This helps stakeholders understand individual arguments from the collective perspective of the group. A framework is developed to derive the collective assessment score of individual arguments in a tree using the argumentation reduction inference system. Further, these arguments are clustered using argument strength and collective assessment score to identify clusters of arguments with collective support and collective attack. Identifying outlier opinions in an argumentation tree helps in understanding opinions that are further away from the mean group opinion in the opinion space. Outlier opinions may exist from two perspectives in argumentation: individual viewpoint and collective viewpoint of the group. A framework is developed in this dissertation to address this challenge from both perspectives. Evaluation of the methods is also presented and it shows that the proposed methods are effective in identifying polarization groups and outlier opinions. The information produced by these methods help decision makers and stakeholders in making more informed decisions --Abstract, pages iii-iv

Collective intelligence: creating a prosperous world at peace

XXXII, 612 p. ; 24 cmLibro ElectrónicoEn este documento se plantea un tema de interes general mas como lo es especificamente el tema de la evolucion de la sociedad en materia de industria y crecimiento de las actividades humanas en el aspecto de desarrollo de la creatividad enfocada a los mercadosedited by Mark Tovey ; foreword by Yochai Benkler (re-mixed by Hassan Masum) ; prefaces by Thomas Malone, Tom Atlee & Pierre Levy ; afterword by Paul Martin & Thomas Homer-Dixon.The era of collective intelligence has begun in earnest. While others have written about the wisdom of crowds, an army of Davids, and smart mobs, this collection of essays for the first time brings together fifty-five pioneers in the emerging discipline of collective intelligence. They provide a base of tools for connecting people, producing high-functioning teams, collaborating at multiple scales, and encouraging effective peer-production. Emerging models are explored for digital deliberative democracy, self-governance, legislative transparency, true-cost accounting, and the ethical use of open sources and methods. Collective Intelligence is the first of a series of six books, which will also include volumes on Peace Intelligence, Commercial Intelligence, Gift Intelligence, Cultural Intelligence, and Global Intelligence.Table of Contents Dedication i Publisher’s Preface iii Foreword by Yochai Benkler Remix Hassan Masum xi The Wealth of Networks: Highlights remixed Editor’s Preface xxi Table of Contents xxv A What is collective intelligence and what will we do 1 about it? (Thomas W. Malone, MIT Center for Collective Intelligence) B Co-Intelligence, collective intelligence, and conscious 5 evolution (Tom Atlee, Co-Intelligence Institute) C A metalanguage for computer augmented collective 15 intelligence (Prof. Pierre Lévy, Canada Research Chair in Collective Intelligence, FRSC) I INDIVIDUALS & GROUPS I-01 Foresight I-01-01 Safety Glass (Karl Schroeder, science fiction author 23 and foresight consultant) I-01-02 2007 State of the Future (Jerome C. Glenn & 29 Theodore J. Gordon, United Nations Millennium Project) I-02 Dialogue & Deliberation I-02-01 Thinking together without ego: Collective intelligence 39 as an evolutionary catalyst (Craig Hamilton and Claire Zammit, Collective-Intelligence.US) I-02-02 The World Café: Awakening collective intelligence 47 and committed action (Juanita Brown, David Isaacs and the World Café Community) I-02-03 Collective intelligence and the emergence of 55 wholeness (Peggy Holman, Nexus for Change, The Change Handbook) I-02-04 Knowledge creation in collective intelligence (Bruce 65 LaDuke, Fortune 500, HyperAdvance.com) I-02-05 The Circle Organization: Structuring for collective 75 wisdom (Jim Rough, Dynamic Facilitation & The Center for Wise Democracy) I-03 Civic Intelligence I-03-01 Civic intelligence and the public sphere (Douglas 83 Schuler, Evergreen State College, Public Sphere Project) I-03-02 Civic intelligence and the security of the homeland 95 (John Kesler with Carole and David Schwinn, IngeniusOnline) I-03-03 Creating a Smart Nation (Robert Steele, OSS.Net) 107 I-03-04 University 2.0: Informing our collective intelligence 131 (Nancy Glock-Grueneich, HIGHEREdge.org) I-03-05 Producing communities of communications and 145 foreknowledge (Jason “JZ” Liszkiewicz, Reconfigure.org) I-03-06 Global Vitality Report 2025: Learning to transform I-04 Electronic Communities & Distributed Cognition I-04-01 Attentional capital and the ecology of online social 163 conflict and think together effectively (Peter+Trudy networks (Derek Lomas, Social Movement Lab, Johnson-Lenz, Johnson-Lenz.com ) UCSD) I-04-02 A slice of life in my virtual community (Howard 173 Rheingold, Whole Earth Review, Author & Educator) I-04-03 Shared imagination (Dr. Douglas C. Engelbart, 197 Bootstrap) I-05 Privacy & Openness I-05-01 We’re all swimming in media: End-users must be able 201 to keep secrets (Mitch Ratcliffe, BuzzLogic & Tetriad) I-05-02 Working openly (Lion Kimbro, Programmer and 205 Activist) I-06 Integral Approaches & Global Contexts I-06-01 Meta-intelligence for analyses, decisions, policy, and 213 action: The Integral Process for working on complex issues (Sara Nora Ross, Ph.D. ARINA & Integral Review) I-06-02 Collective intelligence: From pyramidal to global 225 (Jean-Francois Noubel, The Transitioner) I-06-03 Cultivating collective intelligence: A core leadership 235 competence in a complex world (George Pór, Fellow at Universiteit van Amsterdam) II LARGE-SCALE COLLABORATION II-01 Altruism, Group IQ, and Adaptation II-01-01 Empowering individuals towards collective online 245 production (Keith Hopper, KeithHopper.com) II-01-02 Who’s smarter: chimps, baboons or bacteria? The 251 power of Group IQ (Howard Bloom, author) II-01-03 A collectively generated model of the world (Marko 261 A. Rodriguez, Los Alamos National Laboratory) II-02 Crowd Wisdom and Cognitive Bias II-02-01 Science of CI: Resources for change (Norman L 265 Johnson, Chief Scientist at Referentia Systems, former LANL) II-02-02 Collectively intelligent systems (Jennifer H. Watkins, 275 Los Alamos National Laboratory) II-02-03 A contrarian view (Jaron Lanier, scholar-in-residence, 279 CET, UC Berkeley & Discover Magazine) II-03 Semantic Structures & The Semantic Web II-03-01 Information Economy Meta Language (Interview with 283 Professor Pierre Lévy, by George Pór) II-03-02 Harnessing the collective intelligence of the World- 293 Wide Web (Nova Spivack, RadarNetworks, Web 3.0) II-03-03 The emergence of a global brain (Francis Heylighen, 305 Free University of Brussels) II-04 Information Networks II-04-01 Networking and mobilizing collective intelligence (G. Parker Rossman, Future of Learning Pioneer) II-04-02 Toward high-performance organizations: A strategic 333 role for Groupware (Douglas C. Engelbart, Bootstrap) II-04-03 Search panacea or ploy: Can collective intelligence 375 improve findability? (Stephen E. Arnold, Arnold IT, Inc.) II-05 Global Games, Local Economies, & WISER II-05-01 World Brain as EarthGame (Robert Steele and many 389 others, Earth Intelligence Network) II-05-02 The Interra Project (Jon Ramer and many others) 399 II-05-03 From corporate responsibility to Backstory 409 Management (Alex Steffen, Executive Editor, Worldchanging.com) II-05-04 World Index of Environmental & Social 413 Responsibility (WISER) By the Natural Capital Institute II-06 Peer-Production & Open Source Hardware II-06-01 The Makers’ Bill of Rights (Jalopy, Torrone, and Hill) 421 II-06-02 3D Printing and open source design (James Duncan, 423 VP of Technology at Marketingisland) II-06-03 REBEARTHTM: 425 II-07 Free Wireless, Open Spectrum, and Peer-to-Peer II-07-01 Montréal Community Wi-Fi (Île Sans Fil) (Interview 433 with Michael Lenczner by Mark Tovey) II-07-02 The power of the peer-to-peer future (Jock Gill, 441 Founder, Penfield Gill Inc.) Growing a world 6.6 billion people would want to live in (Marc Stamos, B-Comm, LL.B) II-07-03 Open spectrum (David Weinberger) II-08 Mass Collaboration & Large-Scale Argumentation II-08-01 Mass collaboration, open source, and social 455 entrepreneurship (Mark Tovey, Advanced Cognitive Engineering Lab, Institute of Cognitive Science, Carleton University) II-08-02 Interview with Thomas Homer-Dixon (Hassan 467 Masum, McLaughlin-Rotman Center for Global Health) II-08-03 Achieving collective intelligence via large-scale argumentation (Mark Klein, MIT Center for Collective Intelligence) II-08-04 Scaling up open problem solving (Hassan Masum & 485 Mark Tovey) D Afterword: The Internet and the revitalization of 495 democracy (The Rt. Honourable Paul Martin & Thomas Homer-Dixon) E Epilogue by Tom Atlee 513 F Three Lists 515 1. Strategic Reading Categories 2. Synopsis of the New Progressives 3. Fifty-Two Questions that Matter G Glossary 519 H Index 52

Social Media and Online Public Deliberation: A Case Study of Climate Change Communication on Twitter

This thesis studies the deliberative potential of social media, focusing on climate change communication on Twitter. In particular, this study seeks to explore the online deliberation seen in users' interactions and user-generated content from the perspectives of social network analysis and framing. Three research questions will be answered by three case studies focusing on climate change and an emerging technological topic related to climate change (negative emissions, intentional human efforts to remove CO2 emissions from the atmosphere): how did the climate strikes impact the deliberative potential of climate change discussions online, how did users collectively frame climate change via hashtags, and how did different user groups on Twitter collectively frame negative emissions via tweets? Together, these three questions allow the construction of a picture on the overarching research question: what is the potential of online discussions for deliberation? The data was collected using Twitter's application programming interfaces, covering, for the general topic of climate change, the period 10 September 2018 to 10 September 2019 and, for the subtopic of negative emissions, the period 10 June 2019 to 10 September 2019. There are three main findings of this thesis. First, it shows the changes of deliberative potential of climate change discussions before and after climate strikes and provides evidence that climate strikes increased the potential for deliberation by increasing reciprocity and diversity within the discussion of climate change. However, discussion of climate change after the climate strikes appears to have had less deliberative equality. Second, the thesis reveals that users collectively frame climate change by selecting and associating different hashtags in tweets. In particular, users utilise different hashtags to serve different framing purposes. For example, they use hashtags about consequences, causes and solutions of climate change to spread meaning throughout the entire hashtag occurrence network. Users also tend to connect less popular hashtags with more popular hashtags and make the latter even more popular, and tend to connect hashtags in the same category together in general. The thesis also characterises how climate change is framed on Twitter. In particular, it shows evidence that users tend to frame climate change as a problem that we can solve, and highlights the need for further action. Third, the thesis provides insights into negative emissions as an emerging technological topic, perhaps not as studied from a communication and social impact perspective as is warranted. The frames identified by structural topic modelling show various concerns of different user groups, such as governments, the media and business, and give us clues to the current situation of the communication and acceptance of negative emissions. As it focuses on the politics of climate change in the English language, the findings can not be generalised to all situations. However, it provides a research framework based on social network analysis and framing to examine the deliberative potential of online discussions and contribute to the understanding of climate change communication practice. This thesis provides a basis for future research that is expected to measure online deliberation more comprehensively and thoroughly, and improve our understanding of how social media is used by publics to communally work through the issues of climate change

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Towards adaptive argumentation learning systems : theoretical and practical considerations in the design of argumentation learning systems

This dissertation addresses four issues of pivotal importance in realizing the promises of adaptive argumentation learning systems: (1) User interface: How can argumentation user interfaces be designed to effectively structure and support problem solving, peer interaction, and learning? (2) Software architecture: How can software architectures of adaptive argumentation learning systems be designed to be employable across different argumentation domains and application scenarios in a flexible and cost-effective manner? (3) Diagnostics: How can user behavior be analyzed, automatically and accurately, to drive automated adaptations and help generation? (4) Adaptation: How can strategies for automated adaptation and support be designed to promote problem solving, peer interaction, and learning in an optimal fashion? Regarding issue (1), this dissertation investigates argument diagrams and structured discussion interfaces, two areas of focal interest in argumentation learning research during the past decades. The foundation for such structuring approaches is given by theories of learning and teaching with knowledge representations (theory of representational guidance) and collaboration scripts (script theory of guidance in computer-supported collaborative learning). This dissertation brings these two strands of research together and presents a computer-based learning environment that combines both approaches to support students in conducting high-quality discussions of controversial texts. An empirical study confirms that this combined approach has positive impact on the quality of discussions, thus, underpins the theoretical basis of the approach. Regarding issue (2), this dissertation presents a software framework for enhancing argumentation systems with adaptive support mechanisms. Adaptive support functionality of past argumentation systems has been tailored to particular domains and application scenarios. A novel software framework is presented that abstracts from the specific demands of different domains and application scenarios to provide a more general approach. The approach comprises an extensive configuration subsystem that allows the flexible definition of intelligent software agents, that is, software components able to reason and act autonomously to help students engage in fruitful learning activities. A graphical authoring tool has been conceptualized and implemented to simplify the process of defining and administering software agents beyond what has been achieved with the provided framework system. Among other things, the authoring tool allows, for the first time, specifying relevant patterns in argument diagrams using a graphical language. Empirical results indicate the high potential of the authoring approach but also challenges for future research. Regarding issue (3), the dissertation investigates two alternative approaches to automatically analyzing argumentation learning activities: the knowledge-driven and the data-driven analysis method. The knowledge-driven approach utilizes a pattern search component to identify relevant structures in argument diagrams based on declarative pattern specifications. The capabilities and appropriateness of this approach are demonstrated through three exemplary applications, for which pedagogically relevant patterns have been defined and implemented within the component. The approach proves particularly useful for patterns of limited complexity in scenarios with sufficient expert knowledge available. The data-driven approach is based on machine learning techniques, which have been employed to induce computational classifiers for important aspects of graphical online discussions, such as off-topic contributions, reasoned claims, and question-answer interactions. Validation results indicate that this approach can be realistically used even for complex classification tasks involving natural language. This research constitutes the first investigation on the use of machine learning techniques to analyze diagram-based educational discussions. The dissertation concludes with discussing the four addressed research challenges in the broader context of existing theories and empirical results. The pros and cons of different options in the design of argumentation learning systems are juxtaposed; areas for future research are identified. This final part of the dissertation gives researchers and practitioners a synopsis of the current state of the art in the design of argumentation learning systems and its theoretical and empirical underpinning. Special attention is paid to issue (4), with an in-depth discussion of existing adaptation approaches and corresponding empirical results.Diese Dissertationsschrift behandelt die folgenden vier Fragestellungen, welche bei der Realisierung adaptiver Argumentationssysteme von zentraler Bedeutung sind: (1) Benutzerschnittstelle: Wie müssen Benutzerschnittstellen beschaffen sein, um Problemlöse-, Kooperations- und Lernprozesse effektiv zu strukturieren und zu unterstützen? (2) Softwarearchitektur: Wie können die Funktionalitäten eines adaptiven Argumentationslernsystems in eine Softwarearchitektur abgebildet werden, welche flexibel und mit angemessenem Aufwand in verschiedenen Bereichen und Szenarien einsetzbar ist? (3) Diagnostik: Wie kann Benutzerverhalten automatisch und mit hoher Genauigkeit analysiert werden, um automatisierte Anpassungen und Hilfestellungen effektiv zu steuern? (4) Adaption: Wie sollten automatisierte Anpassungen und Hilfestellungen ausgestaltet werden, um Problemlöse-, Kooperations- und Lernprozesse optimal zu unterstützen? Hinsichtlich Fragestellung (1) untersucht diese Arbeit Argumentationsdiagramme und strukturierte Onlinediskussionen, zwei Schwerpunkte der Forschung zu Lernsystemen für Argumentation der vergangenen Jahre. Die Grundlage solcher Strukturierungsansätze bilden Theorien zum Lehren und Lernen mit Wissensrepräsentationen (theory of representational guidance) und Kooperationsskripten (script theory of guidance in computer-supported collaborative learning). Diese Arbeit führt beide Forschungsstränge in einer neuartigen Lernumgebung zusammen, die beide Ansätze vereint, um Lernende beim Diskutieren kontroverser Texte zu unterstützen. Eine empirische Untersuchung zeigt, dass sich dieser kombinierte Ansatz positiv auf die Diskussionsqualität auswirkt und bekräftigt damit die zu Grunde liegenden theoretischen Annahmen. Hinsichtlich Fragestellung (2) stellt diese Arbeit ein Software-Rahmensystem zur Bereitstellung adaptiver Unterstützungsmechanismen in Argumentationssystemen vor. Das Rahmensystem abstrahiert von domänen- und anwendungsspezifischen Besonderheiten und stellt damit einen generelleren Ansatz im Vergleich zu früheren Systemen dar. Der Ansatz umfasst ein umfangreiches Konfigurationssystem zur Definition intelligenter Softwareagenten, d. h. Softwarekomponenten, die eigeständig schlussfolgern und handeln, um Lernprozesse zu unterstützen. Um das Definieren und Administrieren von Softwareagenten über das bereitgestellte Rahmensystem hinaus zu vereinfachen, wurde ein grafisches Autorenwerkzeug konzipiert und entwickelt. Unter anderem erlaubt dieses erstmals, relevante Muster in Argumentationsdiagrammen ohne Programmierung mittels einer grafischen Sprache zu spezifizieren. Empirische Befunde zeigen neben dem hohen Potential des Ansatzes auch die Notwendigkeit weiterführender Forschung. Hinsichtlich Fragestellung (3) untersucht diese Arbeit zwei alternative Ansätze zur automatisierten Analyse von Lernaktivitäten im Bereich Argumentation: die wissensbasierte und die datenbasierte Analysemethodik. Der wissensbasierte Ansatz wurde mittels einer Softwarekomponente zur Mustersuche in Argumentationsdiagrammen umgesetzt, welche auf Grundlage deklarativer Musterbeschreibungen arbeitet. Die Möglichkeiten und Eignung des Ansatzes werden anhand von drei Beispielszenarien demonstriert, für die verschiedenartige, pädagogisch relevante Muster innerhalb der entwickelten Softwarekomponente definiert wurden. Der Ansatz erweist sich insbesondere als nützlich für Muster eingeschränkter Komplexität in Szenarien, für die Expertenwissen in ausreichendem Umfang verfügbar ist. Der datenbasierte Ansatz wurde mittels maschineller Lernverfahren umgesetzt. Mit deren Hilfe wurden Klassifikationsroutinen zur Analyse zentraler Aspekte von Onlinediskussionen, wie beispielsweise themenfremde Beiträge, begründete Aussagen und Frage-Antwort-Interaktionen, algorithmisch hergeleitet. Validierungsergebnisse zeigen, dass sich dieser Ansatz selbst für komplexe Klassifikationsprobleme eignet, welche die Berücksichtigung natürlicher Sprache erfordern. Dies ist die erste Arbeit zum Einsatz maschineller Lernverfahren zur Analyse von diagrammbasierten Lerndiskussionen. Die Arbeit schließt mit einer Diskussion des aktuellen Forschungsstands hinsichtlich der vier Fragestellungen im breiteren Kontext existierender Theorien und empirischer Befunde. Die Vor- und Nachteile verschiedener Optionen für die Gestaltung von Lernsystemen für Argumentation werden gegenübergestellt und zukünftige Forschungsfelder vorgeschlagen. Dieser letzte Teil der Arbeit bietet Forschern und Anwendern einen umfassenden Überblick des aktuellen Forschungsstands bezüglich des Designs computerbasierter Argumentationslernsysteme und den zugrunde liegenden lehr- und lerntheoretischen Erkenntnissen. Insbesondere wird auf Fragestellung (4) vertiefend eingegangen und bisherige Adaptionsansätze einschließlich entsprechender empirischer Befunde erörtert

Online Deliberation: Design, Research, and Practice

Can new technology enhance purpose-driven, democratic dialogue in groups, governments, and societies? Online Deliberation: Design, Research, and Practice is the first book that attempts to sample the full range of work on online deliberation, forging new connections between academic research, technology designers, and practitioners. Since some of the most exciting innovations have occurred outside of traditional institutions, and those involved have often worked in relative isolation from each other, work in this growing field has often failed to reflect the full set of perspectives on online deliberation. This volume is aimed at those working at the crossroads of information/communication technology and social science, and documents early findings in, and perspectives on, this new field by many of its pioneers. CONTENTS: Introduction: The Blossoming Field of Online Deliberation (Todd Davies, pp. 1-19) Part I - Prospects for Online Civic Engagement Chapter 1: Virtual Public Consultation: Prospects for Internet Deliberative Democracy (James S. Fishkin, pp. 23-35) Chapter 2: Citizens Deliberating Online: Theory and Some Evidence (Vincent Price, pp. 37-58) Chapter 3: Can Online Deliberation Improve Politics? Scientific Foundations for Success (Arthur Lupia, pp. 59-69) Chapter 4: Deliberative Democracy, Online Discussion, and Project PICOLA (Public Informed Citizen Online Assembly) (Robert Cavalier with Miso Kim and Zachary Sam Zaiss, pp. 71-79) Part II - Online Dialogue in the Wild Chapter 5: Friends, Foes, and Fringe: Norms and Structure in Political Discussion Networks (John Kelly, Danyel Fisher, and Marc Smith, pp. 83-93) Chapter 6: Searching the Net for Differences of Opinion (Warren Sack, John Kelly, and Michael Dale, pp. 95-104) Chapter 7: Happy Accidents: Deliberation and Online Exposure to Opposing Views (Azi Lev-On and Bernard Manin, pp. 105-122) Chapter 8: Rethinking Local Conversations on the Web (Sameer Ahuja, Manuel Pérez-Quiñones, and Andrea Kavanaugh, pp. 123-129) Part III - Online Public Consultation Chapter 9: Deliberation in E-Rulemaking? The Problem of Mass Participation (David Schlosberg, Steve Zavestoski, and Stuart Shulman, pp. 133-148) Chapter 10: Turning GOLD into EPG: Lessons from Low-Tech Democratic Experimentalism for Electronic Rulemaking and Other Ventures in Cyberdemocracy (Peter M. Shane, pp. 149-162) Chapter 11: Baudrillard and the Virtual Cow: Simulation Games and Citizen Participation (Hélène Michel and Dominique Kreziak, pp. 163-166) Chapter 12: Using Web-Based Group Support Systems to Enhance Procedural Fairness in Administrative Decision Making in South Africa (Hossana Twinomurinzi and Jackie Phahlamohlaka, pp. 167-169) Chapter 13: Citizen Participation Is Critical: An Example from Sweden (Tomas Ohlin, pp. 171-173) Part IV - Online Deliberation in Organizations Chapter 14: Online Deliberation in the Government of Canada: Organizing the Back Office (Elisabeth Richard, pp. 177-191) Chapter 15: Political Action and Organization Building: An Internet-Based Engagement Model (Mark Cooper, pp. 193-202) Chapter 16: Wiki Collaboration Within Political Parties: Benefits and Challenges (Kate Raynes-Goldie and David Fono, pp. 203-205) Chapter 17: Debian’s Democracy (Gunnar Ristroph, pp. 207-211) Chapter 18: Software Support for Face-to-Face Parliamentary Procedure (Dana Dahlstrom and Bayle Shanks, pp. 213-220) Part V - Online Facilitation Chapter 19: Deliberation on the Net: Lessons from a Field Experiment (June Woong Rhee and Eun-mee Kim, pp. 223-232) Chapter 20: The Role of the Moderator: Problems and Possibilities for Government-Run Online Discussion Forums (Scott Wright, pp. 233-242) Chapter 21: Silencing the Clatter: Removing Anonymity from a Corporate Online Community (Gilly Leshed, pp. 243-251) Chapter 22: Facilitation and Inclusive Deliberation (Matthias Trénel, pp. 253-257) Chapter 23: Rethinking the ‘Informed’ Participant: Precautions and Recommendations for the Design of Online Deliberation (Kevin S. Ramsey and Matthew W. Wilson, pp. 259-267) Chapter 24: PerlNomic: Rule Making and Enforcement in Digital Shared Spaces (Mark E. Phair and Adam Bliss, pp. 269-271) Part VI - Design of Deliberation Tools Chapter 25: An Online Environment for Democratic Deliberation: Motivations, Principles, and Design (Todd Davies, Brendan O’Connor, Alex Cochran, Jonathan J. Effrat, Andrew Parker, Benjamin Newman, and Aaron Tam, pp. 275-292) Chapter 26: Online Civic Deliberation with E-Liberate (Douglas Schuler, pp. 293-302) Chapter 27: Parliament: A Module for Parliamentary Procedure Software (Bayle Shanks and Dana Dahlstrom, pp. 303-307) Chapter 28: Decision Structure: A New Approach to Three Problems in Deliberation (Raymond J. Pingree, pp. 309-316) Chapter 29: Design Requirements of Argument Mapping Software for Teaching Deliberation (Matthew W. Easterday, Jordan S. Kanarek, and Maralee Harrell, pp. 317-323) Chapter 30: Email-Embedded Voting with eVote/Clerk (Marilyn Davis, pp. 325-327) Epilogue: Understanding Diversity in the Field of Online Deliberation (Seeta Peña Gangadharan, pp. 329-358). For individual chapter downloads, go to odbook.stanford.edu