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David Cline provides a detailed analysis of Morton Feldman's graph works and how they changed the course of post-war music.
Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
By modeling pedagogical scenarios as directed geometrical graphs and proposing an associated modeling language, this book describes how rich learning activities, often designed for small classes, can be scaled up for use with thousands of participants. With the vertices of these graphs representing learning activities and the edges capturing the pedagogical relationship between activities, individual, team, and class-wide activities are integrated into a consistent whole. The workflow mechanisms modeled in the graphs enable the construction of scenarios that are richer than those currently implemented in MOOCs. The cognitive states of learners in two consecutive activities feed a transition matrix, which encapsulates the probability of succeeding in the second activity, based on success in the former. This transition matrix is summarized by a numerical value, which is used as the weight of the edge. This pedagogical framework is connected to stochastic models, with the goal of making learning analytics more appealing for data scientists. However, the proposed modeling language is not only useful in learning technologies, it also allows researchers in learning sciences to formally describe the structure of any lesson, from an elementary school lesson with 20 students to an online course with 20,000 participants.
CSCL has in the past 15 years (and often in conjunction with Springer) grown into a thriving and active community. Yet, lacking is a comprehensive CSCL handbook that displays the range of research being done in this area. This handbook will provide an overview of the diverse aspects of the field, allowing newcomers to develop a sense of the entirety of CSCL research and for existing community members to become more deeply aware of work outside their direct area. The handbook will also serve as a ready reference for foundational concepts, methods, and approaches in the field. The chapters are written in such a way that each of them can be used in a stand-alone fashion while also serving as introductory readings in relevant study courses or in teacher education. While some CSCL-relevant topics are addressed in the International Handbook of the Learning Sciences and the International Handbook of Collaborative Learning, these books do not aim to present an integrated and comprehensive view of CSCL. The International Handbook of Computer- Supported Collaborative Learning covers all relevant topics in CSCL, particularly recent developments in the field, such as the rise of computational approaches and learning analytics.
In and out of formal schooling, online and off, today’s learners must consume and integrate a level of information that is exponentially larger and delivered through a wider range of formats and viewpoints than ever before. The Handbook of Learning from Multiple Representations and Perspectives provides a path for understanding the cognitive, motivational, and socioemotional processes and skills necessary for learners across educational contexts to make sense of and use information sourced from varying inputs. Uniting research and theory from education, psychology, literacy, library sciences, media and technology, and more, this forward-thinking volume explores the common concerns, shared challenges, and thematic patterns in our capacity to make meaning in an information-rich society. Chapter 16 of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license available at http://www.taylorfrancis.com/books/e/9780429443961.
The International Handbook of the Learning Sciences is a comprehensive collection of international perspectives on this interdisciplinary field. In more than 50 chapters, leading experts synthesize past, current, and emerging theoretical and empirical directions for learning sciences research. The three sections of the handbook capture, respectively: foundational contributions from multiple disciplines and the ways in which the learning sciences has fashioned these into its own brand of use-oriented theory, design, and evidence; learning sciences approaches to designing, researching, and evaluating learning broadly construed; and the methodological diversity of learning sciences research, assessment, and analytic approaches. This pioneering collection is the definitive volume of international learning sciences scholarship and an essential text for scholars in this area.
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.
This book constitutes the refereed proceedings of the two International Workshops on Big-Graphs Online Querying, Big-O(Q) 2015, and Data Management and Analytics for Medicine and Healthcare, DMAH 2015, held at Waikoloa, Hawaii, USA on August 31 and September 4, 2015, in conjunction with the 41st International Conference on Very Large Data Bases, VLDB 2015. The 9 revised full papers presented together with 5 invited papers and 1 extended abstract were carefully reviewed and selected from 22 initial submissions. The papers are organized in topical sections on information retrieval and data analytics for electronic medical records; data management and visualization of medical data; biomedical data sharing and integration; medical imaging analytics; and big-graphs online querying.
This volume of Advances in Intelligent Systems and Computing highlights key scientific achievements and innovations in all areas of automation, informatization, computer science, and artificial intelligence. It gathers papers presented at the IITI 2017, the Second International Conference on Intelligent Information Technologies for Industry, which was held in Varna, Bulgaria on September 14–16, 2017. The conference was jointly co-organized by Technical University of Varna (Bulgaria), Technical University of Sofia (Bulgaria), VSB Technical University of Ostrava (Czech Republic) and Rostov State Transport University (Russia). The IITI 2017 brought together international researchers and industrial practitioners interested in the development and implementation of modern technologies for automation, informatization, computer science, artificial intelligence, transport and power electrical engineering. In addition to advancing both fundamental research and innovative applications, the conference is intended to establish a new dissemination platform and an international network of researchers in these fields.
Winner of two Outstanding Book Awards from the Association of Educational Communications and Technology (Culture, Learning, & Technology and Systems Thinking & Change divisions)! Rapid advancements in our ability to collect, process, and analyze massive amounts of data along with the widespread use of online and blended learning platforms have enabled educators at all levels to gain new insights into how people learn. Responsible Analytics and Data Mining in Education addresses the thoughtful and purposeful navigation, evaluation, and implementation of these emerging forms of educational data analysis. Chapter authors from around the world explore how data analytics can be used to improve course and program quality; how the data and its interpretations may inadvertently impact students, faculty, and institutions; the quality and reliability of data, as well as the accuracy of data-based decisions; ethical implications surrounding the collection, distribution, and use of student-generated data; and more. This volume unpacks and explores this complex issue through a systematic framework whose dimensions address the issues that must be considered before implementation of a new initiative or program.