Learning analytics has great potential to inform educators in making data-driven decisions at the individual, classroom, institutional, and policy levels. This chapter reviews emerging strands of learning analytics research and state-of-the-art data mining techniques applied in the field. It reports on three general technical approaches to learning analytics and provides case examples of how these have been applied by educational researchers from Asia. Specifically, the reported cases illustrate the use of lag sequential analysis for analyzing learning behaviors, social network analysis for investigating collaborative learning, and data mining techniques for understanding learning processes. We highlight the implications for education emerging from our review, and elaborate on potential areas for future research as well as on the implementation of learning analytics.