Language is of central importance to the field of education because it is a conduit for communicating and understanding information. Therefore, researchers in the field of learning analytics can benefit from methods developed to analyze language both accurately and efficiently. Natural language processing (NLP) techniques can provide such an avenue. NLP techniques are used to provide computational analyses of different aspects of language as they relate to particular tasks. In this chapter, the authors discuss multiple, available NLP tools that can be harnessed to understand discourse, as well as some applications of these tools for education. A primary focus of these tools is the automated interpretation of human language input in order to drive interactions between humans and computers, or human-computer interaction. Thus, the tools measure a variety of linguistic features important for understanding text, including coherence, syntactic complexity, lexical diversity, and semantic similarity. The authors conclude the chapter with a discussion of computer-based learning environments that have employed NLP tools (i.e., ITS, MOOCs, and CSCL) and how such tools can be employed in future research. [This is a chapter in: "Handbook of Learning Analytics," pp. 93-104, 2017, Society for Learning Analytics Research.]