Data-Driven Design for Computer-Supported Collaborative Learning


Book Description

This book highlights the importance of design in computer-supported collaborative learning (CSCL) by proposing data-driven design and assessment. It addresses data-driven design, which focuses on the processing of data and on improving design quality based on analysis results, in three main sections. The first section explains how to design collaborative learning activities based on data-driven design approaches, while the second shares illustrative examples of computer-supported collaborative learning activities. In turn, the third and last section demonstrates how to evaluate design quality and the fidelity of enactment based on design-centered research. The book features several examples of innovative data-driven design approaches to optimizing collaborative learning activities; highlights innovative CSCL activities in authentic learning environments; demonstrates how learning analytics can be used to optimize CSCL design; and discusses the design-centered research approach to evaluating the alignment between design and enactment in CSCL. Given its scope, it will be of interest to a broad readership including researchers, educators, practitioners, and students in the field of collaborative learning, as well as the rapidly growing community of people who are interested in optimizing learning performance with CSCL.




Scripting Computer-Supported Collaborative Learning


Book Description

Theoretically, the term "script" appears to be rather ill-defined. This book clarifies the use of the term "script" in education. It approaches the term from at least three perspectives: cognitive psychology perspective, computer science perspective, and an educational perspective. The book provides learners with scripts that support them both in communication/coordination and in higher-order learning.




Data Driven Approaches in Digital Education


Book Description

This book constitutes the proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, held in Tallinn, Estonia, in September 2017. The 24 full papers, 23 short papers, 6 demo papers, and 22 poster papers presented in this volume were carefully reviewed and selected from 141 submissions. The theme for the 12th EC-TEL conference on Data Driven Approaches in Digital Education' aims to explore the multidisciplinary approaches thateectively illustrate how data-driven education combined with digital education systems can look like and what are the empirical evidences for the use of datadriven tools in educational practices.







Integrating Artificial Intelligence in Education: Enhancing Teaching Practices for Future Learning


Book Description

The education sector faces unprecedented challenges, from rapidly evolving technologies to diverse learner needs, placing immense pressure on educators to adapt and innovate. Traditional teaching methods need help to keep pace with the demands of modern education, leading to gaps in personalized learning and student engagement. Ethical concerns surrounding AI integration in education remain a significant hurdle, requiring careful navigation and responsible implementation. Integrating Artificial Intelligence in Education: Enhancing Teaching Practices for Future Learning offers a comprehensive solution by exploring how AI can address these challenges and revolutionize education. Through a collection of insightful contributions, it provides practical strategies for integrating AI into teaching practices, empowering educators to personalize learning experiences and enhance student engagement. By examining AI ethics and responsible education, the book equips educators with the knowledge needed to navigate the ethical complexities of AI integration.




Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support


Book Description

This book constitutes the proceedings of the 4th International Conference on Decision Support Systems, ICDSST 2018, held in Heraklion, Greece, in May 2018. The main topic of this year’s conference was “Sustainable Data-Driven and Evidence Based Decision Support”. The 15 papers presented in this volume were carefully reviewed and selected from 71 submissions. They were organized in topical sections named: decision support systems for a sustainable society; decision support systems serving the public; decision support systems in management and organization; and advances in decision support systems’ technologies and methods. The EWG-DSS series of International Conference on Decision Support System Technology (ICDSST), starting with ICDSST 2015 in Belgrade, were planned to consolidate the tradition of annual events organized by the EWG-DSS in offering a platform for European and international DSS communities, comprising the academic and industrial sectors, to present state-of-the-art DSS research and developments, to discuss current challenges that surround decision-making processes, to exchange ideas about realistic and innovative solutions, and to co-develop potential business opportunities.







Studying Virtual Math Teams


Book Description

Studying Virtual Math Teams centers on detailed empirical studies of how students in small online groups make sense of math issues and how they solve problems by making meaning together. These studies are woven together with materials that describe the online environment and pedagogical orientation, as well as reflections on the theoretical implications of the findings in the studies. The nature of group cognition and shared meaning making in collaborative learning is a foundational research issue in CSCL. More generally, the theme of sense making is a central topic in information science. While many authors allude to these topics, few have provided this kind of detailed analysis of the mechanisms of intersubjective meaning making. This book presents a coherent research agenda that has been pursued by the author and his research group. The book opens with descriptions of the project and its methodology, as well as situating this research in the past and present context of the CSCL research field. The core research team then presents five concrete analyses of group interactions in different phases of the Virtual Math Teams research project. These chapters are followed by several studies by international collaborators, discussing the group discourse, the software affordances and alternative representations of the interaction, all using data from the VMT project. The concluding chapters address implications for the theory of group cognition and for the methodology of the learning sciences. In addition to substantial introductory and concluding chapters, this important new book includes analyses based upon the author's previous research, thereby providing smooth continuity and an engaging flow that follows the progression of the research. The VMT project has dual goals: (a) to provide a source of experience and data for practical and theoretical explorations of group knowledge building and (b) to develop an effective online environment and educational service for collaborative learning of mathematics. Studying Virtual Math Teams reflects these twin orientations, reviewing the intertwined aims and development of a rigorous science of small-group cognition and a Web 2.0 educational math service. It documents the kinds of interactional methods that small groups use to explore math issues and provides a glimpse into the potential of online interaction to promote productive math discourse.




Intelligent Adaptation and Personalization Techniques in Computer-Supported Collaborative Learning


Book Description

Adaptation and personalization have been extensively studied in CSCL research community aiming to design intelligent systems that adaptively support eLearning processes and collaboration. Yet, with the fast development in Internet technologies, especially with the emergence of new data technologies and the mobile technologies, new opportunities and perspectives are opened for advanced adaptive and personalized systems. Adaptation and personalization are posing new research and development challenges to nowadays CSCL systems. In particular, adaptation should be focused in a multi-dimensional way (cognitive, technological, context-aware and personal). Moreover, it should address the particularities of both individual learners and group collaboration. As a consequence, the aim of this book is twofold. On the one hand, it discusses the latest advances and findings in the area of intelligent adaptive and personalized learning systems. On the other hand it analyzes the new implementation perspectives for intelligent adaptive learning and collaborative systems that are brought by the advances in scripting languages, IMS LD, educational modeling languages and learning activity management systems. Given the variety of learning needs as well as the existence of different technological solutions, the book exemplifies the methodologies and best practices through several case studies and adaptive real-world collaborative learning scenarios, which show the advancement in the field of analysis, design and implementation of intelligent adaptive and personalized systems.




Data Cultures in Higher Education


Book Description

This collection focuses on the role of higher education institutions concerning datafication as a complex phenomenon. It explores how the universities can develop data literac(ies) shaping tomorrow skills and “formae mentis” to face the most deleterious effects of datafication, but also to engage in creative and constructive ways with data. Notably, the book spots data practices within the two most relevant sides of academics’ professional practice, namely, research and teaching. Hence, the collection seeks to reflect on faculty’s professional learning about data infrastructures and practices. The book draws on a range of studies covering the higher education response to the several facets of data in society, from data surveillance and the algorithmic control of human behaviour to empowerment through the use of open data. The research reported ranges from literature overviews to multi-case and in-depth case studies illustrating institutional and educational responses to different problems connected to data. The ultimate intention is to provide conceptual bases and practical examples relating to universities’ faculty development policies to overcome data practices and discourses' fragmentation and contradictions: in a nutshell, to build “fair data cultures” in higher education.