With the rapid development of Internet technologies and sophisticated online learning environments, increasing volumes and varieties of data are being generated, and data analysis has moved on to more complex analysis techniques, such as educational data mining and learning analytics.

SECTION I: Strategies and Methodologies based on Learning Data Analysis.-Chapter 1. Predictive Analytics: Another Vision of the Learning Process.-Chapter 2. A Procedural Learning and Institutional Analytics Framework.-Chapter 3. Extending learning analytics with microlevel student engagement data.-Chapter 4. Learning analytics in mobile applications based on multimodal interaction.-SECTION II: Applications of Analytics and Awareness Learning Services to eLearning.-Chapter 5. The role of data analytics in m-learning conversational applications.-Chapter 6. Enhancing Virtual Learning Spaces: the impact of the Gaming Analytics.-Chapter 7. Advice for Action with Automatic Feedback System s.-SECTION III: Practical Use Cases and Evaluation in Real Context of eLearning.-Chapter 8. Towards Full Engagement for Open Online Education. A practical experience from MicroMasters at edX.-Chapter 9. A Data Mining Approach to Identify the Factors Affec ting to the Academic Success of Tertiary Students inSri Lanka.-Chapter 10. Evaluating the acceptance of e-learning systems via subjective and objective data analysis.

This book presents original research on analytics and context awareness with regard to providing sophisticated learning services for all stakeholders in the eLearning context. It offers essential information on the definition, modeling, development and deployment of services for these stakeholders. Data analysis has long-since been a cornerstone of eLearning, supplying learners, teachers, researchers, managers and policymakers with valuable information on learning activities and design. With the rapid development of Internet technologies and sophisticated online learning environments, increasing volumes and varieties of data are being generated, and data analysis has moved on to more complex analysis techniques, such as educational data mining and learning analytics. Now powered by cloud technologies, online learning environments are capable of gathering and storing massive amounts of data in various formats, of tracking user-system and user-user interactions, and of delivering rich contextual information.

Explores the state of the art in software tools and innovative learning strategies for providing effective and efficient solutions to the various problems and challenges currently facing eLearning from a software data engineering perspective Highlights stimulating practical research from leading international experts Provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners