|Author||: Santi Caballé,Stavros N. Demetriadis,Eduardo Gómez-Sánchez,Pantelis M. Papadopoulos,Armin Weinberger|
|Publisher||: Academic Press|
|Release Date||: 2021-05-01|
|ISBN 10||: 0128231270|
|Pages||: 450 pages|
Intelligent Systems and Learning Data Analytics in Online Education provides novel AI and analytics-based methods to improve online teaching and learning, addressing key problems such as attrition in MOOCs and in online learning. The book explores the state-of-the-art, artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the different problems and challenges in e-Learning and MOOC education. By presenting stimulating theoretical and practical research from leading international experts, this advanced publication provides useful references for educational institutions, academic researchers, professionals, developers and practitioners to apply, evaluate and reproduce. Presents applications of innovative artificial intelligence techniques to collaborative learning activities Offers strategies to provide automatic and effective tutoring to student activities Provides methods to collect, analyze and correctly visualize learning data in educational environments
|Author||: Vijayan Sugumaran,Zheng Xu,Huiyu Zhou|
|Publisher||: Springer Nature|
|Release Date||: 2020-08-09|
|ISBN 10||: 3030514315|
|Pages||: 791 pages|
This book presents the proceedings of the 2020 International Conference on Intelligent Systems Applications in Multi-modal Information Analytics, held in Changzhou, China, on June 18–19, 2020. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering. It addresses a number of broad themes, including data mining, multi-modal informatics, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The contributions cover a wide range of topics such as AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals, and a useful reference guide for newcomers to the field.
|Author||: Zuzana Kubincová|
|Publisher||: Springer Nature|
|ISBN 10||: 3030522873|
|Pages||: 329 pages|
|Author||: Ahmed Tlili,Maiga Chang|
|Publisher||: Springer Nature|
|Release Date||: 2019-09-10|
|ISBN 10||: 9813293357|
|Pages||: 255 pages|
Game-based learning environments and learning analytics are attracting increasing attention from researchers and educators, since they both can enhance learning outcomes. This book focuses on the application of data analytics approaches and research on human behaviour analysis in game-based learning environments, namely educational games and gamification systems, to provide smart learning. Specifically, it discusses the purposes, advantages and limitations of applying such approaches in these environments. Additionally, the various smart game-based learning environments presented help readers integrate learning analytics in their educational games and gamification systems to, for instance, assess and model students (e.g. their computational thinking) or enhance the learning process for better outcomes. Moreover, the book presents general guidelines on various aspects, such as collecting data for analysis, game-based learning environment design, system architecture and applied algorithms, which facilitate incorporating learning analytics into educational games and gamification systems. After a general introduction to help readers become familiar with the subject area, the individual chapters each discuss a different aim of applying data analytics approaches in educational games and gamification systems. Lastly, the conclusion provides a summary and presents general guidelines and frameworks to consider when designing smart game-based learning environments with learning analytics.
|Author||: Santi Caballé,Robert Clarisó|
|Publisher||: Morgan Kaufmann|
|Release Date||: 2016-05-10|
|ISBN 10||: 0128036672|
|Pages||: 382 pages|
Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Discusses application of automatic techniques for e-assessment of learning activities Presents strategies to provide immediate and useful feedback on students’ activities Provides methods to collect, analyze, and correctly visualize learning data in educational environments Explains the applications, benefits, and challenges of using gamification techniques in academic contexts Offers solutions to increase students’ participation and performance while lowering drop-out rates and retention levels
|Author||: Mostafa Ezziyyani|
|Publisher||: Springer Nature|
|Release Date||: 2020-01-03|
|ISBN 10||: 3030366537|
|Pages||: 240 pages|
This book contains the latest researches on advanced intelligent systems applied in the field of education presented during the second edition of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) held on July 08–11, 2019, in Marrakech, Morocco. The book proposes new approaches and innovative strategies for the manipulation of data and big data collected from the educational environment, exploiting the analysis tools, algorithms of artificial intelligence, and machine learning techniques, in order to extract results, which allow improving the performance and effectiveness of the education field, which is a strategic lever for sustainable development. The book deals with concepts, strategies, and approaches developed on various current axes of scientific research in the field of education, such as smart e-learning, smart education (smart classroom, smart assessment and smart teaching and learning technologies), massive open online courses (MOOC), courseware design, and development for smart learning, cloud learning, and mobile learning. The book is intended for all actors in the educational sector, namely students, professors, academic researchers, and stakeholders. It constitutes a large-scale forum for the exchange of ideas, approaches, and innovative techniques between these actors on the development and innovation of the field of education with the revolution 4.0. The authors of each chapter report the state of the art of the various topics addressed and present results of their own research, laboratory experiments, and successful applications. The purpose of this session is to share the idea of advanced intelligent systems with appropriate tools and techniques for modeling, management, and decision support in the field of education.
This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most.
|Author||: Tarek Gaber,Aboul Ella Hassanien,Nashwa El-Bendary,Nilanjan Dey|
|Release Date||: 2015-11-09|
|ISBN 10||: 331926690X|
|Pages||: 536 pages|
The conference topics address different theoretical and practical aspects, and implementing solutions for intelligent systems and informatics disciplines including bioinformatics, computer science, medical informatics, biology, social studies, as well as robotics research. The conference also discuss and present solutions to the cloud computing and big data mining which are considered hot research topics. The conference papers discussed different topics – techniques, models, methods, architectures, as well as multi aspect, domain-specific, and new solutions for the above disciplines. The accepted papers have been grouped into five parts: Part I—Intelligent Systems and Informatics, addressing topics including, but not limited to, medical application, predicting student performance, action classification, and detection of dead stained microscopic cells, optical character recognition, plant identification, rehabilitation of disabled people. Part II—Hybrid Intelligent Systems, addressing topics including, but not limited to, EMG signals, text classification, geomagnetic inverse problem, email filtering. Part III—Multimedia Computing and Social Networks, addressing topics including, but not limited to, augmented reality, telepresence robot, video flash matting, community detection, quality images, face thermal image extraction, MRI tumor segmentation. Part V—Cloud Computing and Big Data Mining, discussing topics including, but not limited to, mining on microblogs, query optimization, big data classification, access control, friendsourcing, and assistive technology. Part VI—Swarm Optimization and Its Applications, addressing topics including, but not limited to, solving set covering problem, adaptive PSO for CT liver segmentation, water quality assessment, attribute reduction, fish detection, solving manufacturing cell design problem.
|Author||: Vijayan Sugumaran,Zheng Xu,Shankar P.,Huiyu Zhou|
|Release Date||: 2019-03-29|
|ISBN 10||: 3030157407|
|Pages||: 1513 pages|
This book presents the proceedings of the 2019 International Conference on Intelligent Systems Applications in Multi-modal Information Analytics, held in Shenyang, China on February 19-20, 2019. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including data mining, multi-modal informatics, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The contributions cover a wide range of topics: AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals, and provides a useful reference guide for newcomers to the field.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
|Author||: Rosella Gennari,Pierpaolo Vittorini,Fernando De la Prieta,Tania Di Mascio,Marco Temperini,Ricardo Azambuja Silveira,Demetrio Arturo Ovalle Carranza|
|Release Date||: 2019-06-24|
|ISBN 10||: 303023990X|
|Pages||: 174 pages|
This book, which gathers the outcomes of the 9th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning and its related workshops, expands on the topics of the evidence-based TEL workshop series in order to provide an open forum for discussing intelligent systems for TEL, their roots in novel learning theories, empirical methodologies for their design and evaluation, stand-alone solutions, and web-based ones. The Conference was hosted by the University of Salamanca and was held in Ávila (Spain) from the 26th to the 28th of June 2019. Its goal was to bring together researchers and developers from industry, education, and the academic world to report on the latest scientific research, technical advances, and methodologies. We wish to thank the sponsors: IEEE Systems Man and Cybernetics Society, Spain Section Chapter and the IEEE Spain Section (Technical Co-Sponsor), IBM, Indra, Viewnext, Global Exchange, AEPIA, APPIA and AIR institute.
|Author||: Aboul Ella Hassanien,Khaled Shaalan,Mohamed Fahmy Tolba|
|Publisher||: Springer Nature|
|Release Date||: 2019-10-02|
|ISBN 10||: 3030311295|
|Pages||: 1090 pages|
This book presents the proceedings of the 5th International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI2019), which took place in Cairo, Egypt, from October 26 to 28, 2019. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: machine learning and applications, swarm optimization and applications, robotic and control systems, sentiment analysis, e-learning and social media education, machine and deep learning algorithms, recognition and image processing, intelligent systems and applications, mobile computing and networking, cyber-physical systems and security, smart grids and renewable energy, and micro-grid and power systems.