Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

On Folding and Twisting (and whatknot): towards a characterization of workspaces in syntax

Syntactic theory has traditionally adopted a constructivist approach, in which a set of atomic elements are manipulated by combinatory operations to yield derived, complex elements. Syntactic structure is thus seen as the result or discrete recursive combinatorics over lexical items which get assembled into phrases, which are themselves combined to form sentences. This view is common to European and American structuralism (e.g., Benveniste, 1971; Hockett, 1958) and different incarnations of generative grammar, transformational and non-transformational (Chomsky, 1956, 1995; and Kaplan & Bresnan, 1982; Gazdar, 1982). Since at least Uriagereka (2002), there has been some attention paid to the fact that syntactic operations must apply somewhere, particularly when copying and movement operations are considered. Contemporary syntactic theory has thus somewhat acknowledged the importance of formalizing aspects of the spaces in which elements are manipulated, but it is still a vastly underexplored area. In this paper we explore the consequences of conceptualizing syntax as a set of topological operations applying over spaces rather than over discrete elements. We argue that there are empirical advantages in such a view for the treatment of long-distance dependencies and cross-derivational dependencies: constraints on possible configurations emerge from the dynamics of the system.Comment: Manuscript. Do not cite without permission. Comments welcom

인공지능과 대화하기: 일대일 그리고 그룹 상용작용을 위한 대화형 에이전트 시스템 개발

학위논문(박사) -- 서울대학교대학원 : 사회과학대학 언론정보학과, 2022.2. 이준환."인간-컴퓨터 상호작용"과 "사용자 경험"을 넘어, "인간-인공지능 상호작용" 그리고 "알고리즘 경험"의 시대가 도래하고 있다. 기술의 발전은 우리가 의사소통하고 협업하는 방식의 패러다임을 전환했다. 기계 에이전트는 인간 커뮤니케이션에서 적극적이며 주도적인 역할을 수행한다. 하지만 효과적인 AI 기반 커뮤니케이션과 토론 시스템 디자인에 대한 이해와 논의는 부족한 것이 사실이다. 이에 본 연구는 인간-컴퓨터 상호작용의 관점에서 다양한 형태의 커뮤니케이션을 지원할 수 있는 기술적 방법을 탐색하는 것을 목표로 한다. 이를 위해 저자는 일대일 그리고 그룹 상호작용을 지원하는 대화형 에이전트를 제시한다. 구체적으로 본 연구는 1) 일대일 상호작요에서 사용자 관여를 높이는 대화형 에이전트, 2) 일상적인 소셜 그룹 토론을 지원하는 에이전트, 3) 숙의 토론을 가능하게 하는 에이전트를 디자인 및 개발하고 그 효과를 정량적 그리고 정성적으로 검증했다. 시스템을 디자인함에 있어서 인간-컴퓨터 상호작용뿐 아니라, 커뮤니케이션학, 심리학, 그리고 데이터 과학을 접목한 다학제적 접근 방식이 적용되었다. 첫 번째 연구는 일대일 상호작용 상황에서 사용자의 관여 증진을 위한 대화형 에이전트의 효과를 검증했다. 설문조사라는 맥락에서 수행된 이 연구는 웹 설문조사에서 응답자의 불성실로 인해 발생하는 응답 데이터 품질의 문제를 극복하기 위한 새로운 인터랙션 방법으로 텍스트 기반 대화형 에이전트의 가능성을 탐색하는 것을 목표로 했다. 이를 위해 2 (인터페이스: 웹 對 챗봇) X 2 (대화 스타일: 포멀 對 캐쥬얼) 실험을 진행했으며, 만족화 이론에 근거하여 응답 데이터의 품질을 평가했다. 그 결과, 챗봇 설문조사의 참여자가 웹 설문조사의 참여자보다 더 높은 수준의 관여를 보이고, 결과적으로 더 높은 품질의 데이터를 생성하는 것을 확인할 수 있었다. 하지만 이런 챗봇의 데이터 품질에 대한 효과는 챗봇이 친구 같고 캐쥬얼한 대화체를 사용할 때만 나타났다. 이 결과는 대화형 인터랙티비티가 인터페이스뿐 아니라 대화 스타일이라는 효과적인 메세지 전략을 동반할 때 발생하는 것을 의미한다. 두 번째 연구는 일상적인 소셜 채팅 그룹에서 집단의 의사결정과정과 토론을 지원하는 대화형 시스템에 대한 것이다. 이를 위해 GroupfeedBot이라는 대화형 에이전트를 제작하였으며, GroupfeedBot은 (1) 토론 시간을 관리하고, (2) 구성원들의 균등한 참여를 촉진하며, (3) 구성원들의 다양한 의견을 요약 및 조직화하는 기능을 갖고 있다. 해당 에이전트를 평가하기 위해 다양한 태스크 (추론, 의사결정, 자유 토론, 문제 해결 과제)와 그룹 규모(소규모, 중규모)에 관하여 사용자 조사를 시행했다. 그 결과 의견의 다양성 측면에서 GroupfeedBot으로 토론한 집단이 기본 에이전트와 토론한 집단보다 더 다양한 의견을 생성했지만 산출된 결과의 품질과 메시지 양에 있어서는 차이가 없는 것을 확인할 수 있었다. 균등한 참여에 대한 GroupfeedBot의 효과는 태스크의 특성에 따라 다르게 나타났는데, 특히 자유 토론 과제에서 GroupfeedBot이 참여자들의 균등한 참여를 촉진했다. 세 번째 연구는 숙의 토론을 지원하는 대화형 시스템에 대한 것이다. 세 번째 연구에서 개발된 DebateBot은 GroupfeeedBot과 달리 더 진지한 사회적 맥락에서 적용되었다. DebateBot은 (1) 생각하기-짝짓기-공유하기 (Think-Pair-Share) 전략에 따라 토론을 구조화하고, (2) 과묵한 토론자에게 의견을 요청함으로써 동등한 참여를 촉진하는 두 가지 주요 기능을 수행했다. 사용자 평가 결과 DebateBot은 그룹 상호작용을 개선함으로써 심의 토론을 가능하게 했다. 토론 구조화는 토론의 질에 긍정적인 효과를 발휘하였고, 참여자 촉진은 진정한 합의 도달에 기여하였으며, 그룹 구성원들의 주관적 만족도를 향상했다. 본 연구는 이 세 가지 연구의 결과들을 바탕으로 인간-인공지능 커뮤니케이션에 대한 다양한 시사점들을 도출하였으며, 이를 TAMED (Task-Agent-Message-Information Exchange-Relationship Dynamics) 모델로 정리하였다.The advancements in technology shift the paradigm of how individuals communicate and collaborate. Machines play an active role in human communication. However, we still lack a generalized understanding of how exactly to design effective machine-driven communication and discussion systems. How should machine agents be designed differently when interacting with a single user as opposed to when interacting with multiple users? How can machine agents be designed to drive user engagement during dyadic interaction? What roles can machine agents perform for the sake of group interaction contexts? How should technology be implemented in support of the group decision-making process and to promote group dynamics? What are the design and technical issues which should be considered for the sake of creating human-centered interactive systems? In this thesis, I present new interactive systems in the form of a conversational agent, or a chatbot, that facilitate dyadic and group interactions. Specifically, I focus on: 1) a conversational agent to engage users in dyadic communication, 2) a chatbot called GroupfeedBot that facilitates daily social group discussion, 3) a chatbot called DebateBot that enables deliberative discussion. My approach to research is multidisciplinary and informed by not only in HCI, but also communication, psychology and data science. In my work, I conduct in-depth qualitative inquiry and quantitative data analysis towards understanding issues that users have with current systems, before developing new computational techniques that meet those user needs. Finally, I design, build, and deploy systems that use these techniques to the public in order to achieve real-world impact and to study their use by different usage contexts. The findings of this thesis are as follows. For a dyadic interaction, participants interacting with a chatbot system were more engaged as compared to those with a static web system. However, the conversational agent leads to better user engagement only when the messages apply a friendly, human-like conversational style. These results imply that the chatbot interface itself is not quite sufficient for the purpose of conveying conversational interactivity. Messages should also be carefully designed to convey such. Unlike dyadic interactions, which focus on message characteristics, other elements of the interaction should be considered when designing agents for group communication. In terms of messages, it is important to synthesize and organize information given that countless messages are exchanged simultaneously. In terms of relationship dynamics, rather than developing a rapport with a single user, it is essential to understand and facilitate the dynamics of the group as a whole. In terms of task performance, technology should support the group's decision-making process by efficiently managing the task execution process. Considering the above characteristics of group interactions, I created the chatbot agents that facilitate group communication in two different contexts and verified their effectiveness. GroupfeedBot was designed and developed with the aim of enhancing group discussion in social chat groups. GroupfeedBot possesses the feature of (1) managing time, (2) encouraging members to participate evenly, and (3) organizing the members’ diverse opinions. The group which discussed with GroupfeedBot tended to produce more diverse opinions compared to the group discussed with the basic chatbot. Some effects of GroupfeedBot varied by the task's characteristics. GroupfeedBot encouraged the members to contribute evenly to the discussions, especially for the open-debating task. On the other hand, DebateBot was designed and developed to facilitate deliberative discussion. In contrast to GroupfeedBot, DebateBot was applied to more serious and less casual social contexts. Two main features were implemented in DebateBot: (1) structure discussion and (2) request opinions from reticent discussants.This work found that a chatbot agent which structures discussions and promotes even participation can improve discussions, resulting in higher quality deliberative discussion. Overall, adding structure to the discussion positively influenced the discussion quality, and the facilitation helped groups reach a genuine consensus and improved the subjective satisfaction of the group members. The findings of this thesis reflect the importance of understanding human factors in designing AI-infused systems. By understanding the characteristics of individual humans and collective groups, we are able to place humans at the heart of the system and utilize AI technology in a human-friendly way.1. Introduction 1.1 Background 1.2 Rise of Machine Agency 1.3 Theoretical Framework 1.4 Research Goal 1.5 Research Approach 1.6 Summary of Contributions 1.7 Thesis Overview 2. Related Work 2.1 A Brief History of Conversational Agents 2.2 TAMED Framework 3. Designing Conversational Agents for Dyadic Interaction 3.1 Background 3.2 Related Work 3.3 Method 3.4 Results 3.5 Discussion 3.6 Conclusion 4. Designing Conversational Agents for Social Group Discussion 4.1 Background 4.2 Related Work 4.3 Needfinding Survey for Facilitator Chatbot Agent 4.4 GroupfeedBot: A Chatbot Agent For Facilitating Discussion in Group Chats 4.5 Qualitative Study with Small-Sized Group 4.6 User Study With Medium-Sized Group 4.7 Discussion 4.8 Conclusion 5. Designing Conversational Agents for Deliberative Group Discussion 5.1 Background 5.2 Related Work 5.3 DebateBot 5.4 Method 5.5 Results 5.6 Discussion and Design Implications 5.7 Conclusion 6. Discussion 6.1 Designing Conversational Agents as a Communicator 6.2 Design Guidelines Based on TAMED Model 6.3 Technical Considerations 6.4 Human-AI Collaborative System 7. Conclusion 7.1 Research Summary 7.2 Summary of Contributions 7.3 Future Work 7.4 Conclusion박

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Proceedings of the ECCS 2005 satellite workshop: embracing complexity in design - Paris 17 November 2005

Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr). Embracing complexity in design is one of the critical issues and challenges of the 21st century. As the realization grows that design activities and artefacts display properties associated with complex adaptive systems, so grows the need to use complexity concepts and methods to understand these properties and inform the design of better artifacts. It is a great challenge because complexity science represents an epistemological and methodological swift that promises a holistic approach in the understanding and operational support of design. But design is also a major contributor in complexity research. Design science is concerned with problems that are fundamental in the sciences in general and complexity sciences in particular. For instance, design has been perceived and studied as a ubiquitous activity inherent in every human activity, as the art of generating hypotheses, as a type of experiment, or as a creative co-evolutionary process. Design science and its established approaches and practices can be a great source for advancement and innovation in complexity science. These proceedings are the result of a workshop organized as part of the activities of a UK government AHRB/EPSRC funded research cluster called Embracing Complexity in Design (www.complexityanddesign.net) and the European Conference in Complex Systems (complexsystems.lri.fr)