Chapter 1. Open learning analytics: a systematic literature review and future perspectives -- Chapter 2. Non-distracting feedback in artificial intelligence supported learning -- Chapter 3. Research on human-computer cooperative teaching supported by artificial intelligence robot assistant -- Chapter 4. A new conceptual framework for measuring online listening in asynchronous discussion forum -- Chapter 5. Self-improvable, self-improving, and self-improvability adaptive instructional system -- Chapter 6. Can sensors effectively support learning? -- Chapter 7. A prototype system of search: finding short material for science education in long and high-definition documentary videos -- Chapter 8. A learning attention monitoring system via photoplethysmogram using wearable wrist devices -- Chapter 9. Towards improving social interaction ability for children with autism spectrum disorder using multimodal sensory information -- Chapter 10. Personalized citation recommendation using a ensemble model of dssm and bibliographic information -- Chapter 11. Augmented: Academic performance prediction based on digital campus -- Chapter 12. Joint embedding learning of educational knowledge graphs -- Chapter 13. Modeling the self-regulated learning behaviors of graduate students in online academic reading and writing environments -- Chapter 14. Mapping machine-generated questions to their related paragraphs in the textbook -- Chapter 15. Change management for learning analytics -- Chapter 16. Lessons learned from designing adaptive training systems: An ethical perspective.

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters. Provides cross-cultural perspectives on artificial intelligence applications in education Offers contributions by distinguished researchers from computer science and learning science Provides an broad view on both systems/algorithms and educational aspects of current challenges in AI-supported educational technologies.