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Author: Michail Giannakos Publisher: Springer Nature ISBN: 3031080769 Category : Education Languages : en Pages : 362
Book Description
This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this “virtual nature” allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated feedback to enable learner self-reflection. However, with this increased complexity in data, new challenges also arise. Conducting the data gathering, pre-processing, analysis, annotation and sense-making, in a way that is meaningful for learning scientists and other stakeholders (e.g., students or teachers), still pose challenges in this emergent field. This handbook aims to serve as a unique resource for state of the art methods and processes. Chapter 11 of this book is available open access under a CC BY 4.0 license at link.springer.com.
Author: Michail Giannakos Publisher: Springer Nature ISBN: 3031080769 Category : Education Languages : en Pages : 362
Book Description
This handbook is the first book ever covering the area of Multimodal Learning Analytics (MMLA). The field of MMLA is an emerging domain of Learning Analytics and plays an important role in expanding the Learning Analytics goal of understanding and improving learning in all the different environments where it occurs. The challenge for research and practice in this field is how to develop theories about the analysis of human behaviors during diverse learning processes and to create useful tools that could augment the capabilities of learners and instructors in a way that is ethical and sustainable. Behind this area, the CrossMMLA research community exchanges ideas on how we can analyze evidence from multimodal and multisystem data and how we can extract meaning from this increasingly fluid and complex data coming from different kinds of transformative learning situations and how to best feed back the results of these analyses to achieve positive transformative actions on those learning processes. This handbook also describes how MMLA uses the advances in machine learning and affordable sensor technologies to act as a virtual observer/analyst of learning activities. The book describes how this “virtual nature” allows MMLA to provide new insights into learning processes that happen across multiple contexts between stakeholders, devices and resources. Using such technologies in combination with machine learning, Learning Analytics researchers can now perform text, speech, handwriting, sketches, gesture, affective, or eye-gaze analysis, improve the accuracy of their predictions and learned models and provide automated feedback to enable learner self-reflection. However, with this increased complexity in data, new challenges also arise. Conducting the data gathering, pre-processing, analysis, annotation and sense-making, in a way that is meaningful for learning scientists and other stakeholders (e.g., students or teachers), still pose challenges in this emergent field. This handbook aims to serve as a unique resource for state of the art methods and processes. Chapter 11 of this book is available open access under a CC BY 4.0 license at link.springer.com.
Author: Carey Jewitt Publisher: ISBN: 9781138245198 Category : Communication Languages : en Pages : 0
Book Description
"The Handbook includes chapters on key themes within multimodality such as technology, culture, notions of identity, social justice and power, and macro issues such as literacy policy. Taking a broad look at multimodality, the contributors engage with how a variety of other theoretical approaches have looked at multimodal communication and representation, including visual studies, anthropology, conversation analysis, socio-cultural theory, sociolinguistics, new literacy studies, multimodal corpora studies, critical discourse, semiotics and eye-tracking. Detailed multimodal analysis case studies are also included, along with an extensive updated glossary of key terms, to support those new to multimodality and to allow those already engaged in multimodal research to explore the fundamentals further"--Publisher's website.
Author: Kah Phooi Seng Publisher: Springer ISBN: 3319975986 Category : Computers Languages : en Pages : 391
Book Description
This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
Author: Olga Viberg Publisher: Springer Nature ISBN: 3031276469 Category : Education Languages : en Pages : 217
Book Description
This book is about practicable learning analytics, that is able to become a successful part of practice, ultimately leading to improved learning and teaching. The aim of the book is to shift our perspective on learning analytics creation and implementation from that of “designing of” technology to that of “designing for” a system of practice. That is, any successful implementation of learning analytics requires a systematic approach, which the book explains through the lens of the Information Systems Artefact, constituting of the three interdependent artefacts: “technical”, “information” and “social”. The contributions of this book go beyond a consideration of particular humans such as teachers and students, and their individual activities to consider the larger systems of activity of which analytics become part of. The chapters included in this book present different cases of learning analytics implementation across countries, and the related opportunities and challenges related to generalizability of the results. The book is written for designers, students and educators of learning analytics who aim to improve learning and teaching through learning analytics.
Author: Sharon Oviatt Publisher: Morgan & Claypool ISBN: 1970001666 Category : Computers Languages : en Pages : 598
Book Description
The Handbook of Multimodal-Multisensor Interfaces provides the first authoritative resource on what has become the dominant paradigm for new computer interfaces— user input involving new media (speech, multi-touch, gestures, writing) embedded in multimodal-multisensor interfaces. These interfaces support smart phones, wearables, in-vehicle and robotic applications, and many other areas that are now highly competitive commercially. This edited collection is written by international experts and pioneers in the field. It provides a textbook, reference, and technology roadmap for professionals working in this and related areas. This first volume of the handbook presents relevant theory and neuroscience foundations for guiding the development of high-performance systems. Additional chapters discuss approaches to user modeling and interface designs that support user choice, that synergistically combine modalities with sensors, and that blend multimodal input and output. This volume also highlights an in-depth look at the most common multimodal-multisensor combinations—for example, touch and pen input, haptic and non-speech audio output, and speech-centric systems that co-process either gestures, pen input, gaze, or visible lip movements. A common theme throughout these chapters is supporting mobility and individual differences among users. These handbook chapters provide walk-through examples of system design and processing, information on tools and practical resources for developing and evaluating new systems, and terminology and tutorial support for mastering this emerging field. In the final section of this volume, experts exchange views on a timely and controversial challenge topic, and how they believe multimodal-multisensor interfaces should be designed in the future to most effectively advance human performance.
Author: Maria Virvou Publisher: Springer ISBN: 3030137430 Category : Technology & Engineering Languages : en Pages : 230
Book Description
This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Author: Jako Olivier Publisher: AOSIS ISBN: 1928523412 Category : Education Languages : en Pages : 470
Book Description
This book aims to provide an overview of theoretical and practical considerations in terms of self-directed multimodal learning within the university context. Multimodal learning is approached in terms of the levels of multimodality and specifically blended learning and the mixing of modes of delivery (contact and distance education). As such, this publication will provide a unique snapshot of multimodal practices within higher education through a self-directed learning epistemological lens. The book covers issues such as what self-directed multimodal learning entails, mapping of specific publications regarding blended learning, blended learning in mathematics, geography, natural science and computer literacy, comparative experiences in distance education as well as situated and culturally appropriate learning in multimodal contexts. This book provides a unique focus on multimodality in terms of learning and delivery within the context of self-directed learning. Therefore, the publication would not only advance the scholarship of blended and open distance learning in South Africa, but also the contribute to enriching the discourse regarding self-direction. From this book readers will get an impression of the latest trends in literature in terms of multimodal self-directed learning in South Africa as well as unique empirical work being done in this regard.
Author: Durak, Gürhan Publisher: IGI Global ISBN: 1668495287 Category : Education Languages : en Pages : 479
Book Description
Maximizing student outcomes in education presents a significant challenge, as traditional assessment methods often fall short in providing actionable insights for improvement. Perspectives on Learning Analytics for Maximizing Student Outcomes addresses this challenge by offering a comprehensive solution. Edited by esteemed scholars Gürhan Durak and Serkan Çankaya, this book provides innovative knowledge and practical experiences on emerging technologies and processes in learning analytics. It covers topics such as data collection, visualization, predictive analytics, and ethical considerations, serving as a guide for academic scholars, technology enthusiasts, and educational institutions. This book empowers professionals and researchers to leverage learning analytics effectively, enabling data-informed decision-making, improved teaching practices, and tailored educational programs. By presenting best practices and future directions, it equips readers with the necessary tools to optimize learning environments and drive student success. With a focus on the transformative potential of learning analytics, this book propels education toward a more efficient and effective system that prioritizes student outcomes.
Author: Michail Giannakos
Author: Michail Giannakos
Author: Carey Jewitt
Author: Mohammed Saqr
Author: Kah Phooi Seng
Author: Olga Viberg
Author: Sharon Oviatt
Author: Maria Virvou
Author: Jako Olivier
Author: Durak, Gürhan
Author: Birgit Pepin