A beginner's guide to learning analytics
著者
書誌事項
A beginner's guide to learning analytics
（Advances in analytics for learning and teaching）
Springer, c2021
- : [hbk]
大学図書館所蔵 全2件
注記
Includes bibliographical references
内容説明・目次
内容説明
This book A Beginner's Guide to Learning Analytics is designed to meet modern educational trends' needs. It is addressed to readers who have no prior knowledge of learning analytics and functions as an introductory text to learning analytics for those who want to do more with evaluation/assessment in their organizations. The book is useful to all who need to evaluate their learning and teaching strategies. It aims to bring greater efficiency and deeper engagement to individual students, learning communities, and educators.
Covered here are the key concepts linked to learning analytics for researchers and practitioners interested in learning analytics. This book helps those who want to apply analytics to learning and development programs and helps educational institutions to identify learners who require support and provide a more personalized learning experience. Like chapters show diverse uses of learning analytics to enhance student and faculty performance. It presents a coherent framework for the effective translation of learning analytics research for educational practice to its practical application in different educational domains. This book provides educators and researchers with the tools and frameworks to effectively make sense of and use data and analytics in their everyday practice. This book will be a valuable addition to researchers' bookshelves.
目次
Chapter 1 Introduction to Learning Analytics 1.1. Introduction to Learning Analytics 1.2. Learning analytics: A new and rapidly developing field 1.3. Benefits and Challenges of learning analytics 1.4. Ethical Concerns with Learning Analytics 1.5. Use of Learning analytics 1.6. Conclusion 1.7. Review QuestionsChapter 2 Educational Data Mining & Learning Analytics 2.1. Introduction 2.2. Educational Data Mining (EDM) 2.3. Educational Data Mining & Learning analytics 2.4. Educational Data Mining & Learning analytics Applications 2.5. Conclusion 2.6. Review QuestionsChapter 3 Preparing for Learning Analytics 3.1. Introduction 3.2. Role of Psychology in Learning analytics 3.3. Architecting the learning analytics environment 3.4. Major Barriers for adopting Learning Analytics 3.5. Case Studies 3.6. Conclusion 3.7. Review QuestionsChapter 4 Data requirements for Learning analytics 4.1. Introduction 4.2. Types of data used for Learning Analytics 4.3. Data Models used to represent usage data for Learning analytics 4.4. Data Privacy maintenance in Learning analytics 4.5. Case Studies 4.6. Conclusion 4.7. Review QuestionsChapter 5 Tools for Learning Analytics 5.1. Introduction 5.2. Popular Learning Analytics Tools 5.3. Choosing a Tool 5.4. Strategies to Successfully Deploy a Tool 5.5. Exploring Learning Analytics Tools 5.6. Case Studies 5.7. Developing a Learning analytics Tool 5.8. Conclusion 5.9. Review QuestionsChapter 6 Other Technology Approaches to Learning Analytics 6.1. Introduction 6.2. Big Data & Learning Analytics 6.3. Data Science & Learning Analytics 6.4. AI & Learning Analytics 6.5. Machine Learning & Learning Analytics 6.6. Deep Learning & Learning Analytics 6.7. Case Studies 6.8. Conclusion 6.9. Review QuestionsChapter 7 Learning Analytics in Massive Open Online Courses 7.1 Introduction to MOOCs 7.2. From MOOCs to Learning analytics 7.3. Integrating Learning analytics with MOOCs 7.4. Benefits of applying Learning Analytics in MOOCs 7.5. Major Concerns of implementing Learning Analytics in MOOCs 7.6. Limitation of Applying Learning Analytics in MOOCs 7.7. Tools that support Leaning analytics in MOOCs 7.8. Case Studies 7.9. Conclusion 7.10. Review QuestionsChapter 8 The Pedagogical perspective of Learning Analytics 8.1. Introduction to Pedagogy 8.2. Learning Analytics based Pedagogical Framework 8.3. Pedagogical Interventions 8.4. Learning Analytics based Pedagogical Models 8.5. Case studies 8.6. Conclusion 8.7. Review QuestionsChapter 9 Moving Forward 9.1. Self-Learning and Learning analytics 9.2. Lifelong learning and learning analytics 9.3. Present and future trend of learning analytics in the world 9.4. Measuring 21st Century Skills using Learning analytics 9.5. Moving Forward 9.6. Smart Learning analytics 9.7. Case Studies 9.8. Conclusion 9.9. Review QuestionsChapter 10 Case Studies 10.1. Recommender systems using learning analytics 10.2. Learning Analytics in Higher Education 10.3. Other Evidences on the use of Learning AnalyticsChapter 11. Problems
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