@incollection{mcnamara_natural_2017,
	address = {Alberta, Canada},
	edition = {1},
	title = {Natural {Language} {Processing} and {Learning} {Analytics}},
	isbn = {978-0-9952408-0-3},
	url = {http://solaresearch.org/hla-17/hla17-chapter1},
	abstract = {Language is of central importance to the  field of education because it is a conduit for com- municating and understanding information. Therefore, researchers in the  eld of learning analytics can bene t from methods developed to analyze language both accurately and ef ciently. 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 di- versity, 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.},
	booktitle = {The {Handbook} of {Learning} {Analytics}},
	publisher = {Society for Learning Analytics Research (SoLAR)},
	author = {McNamara, Danielle and Allen, Laura and Crossley, Scott and Dascalu, Mihai and Perret, Cecile},
	editor = {Lang, Charles and Siemens, George and Wise, Alyssa Friend and Gaševic, Dragan},
	year = {2017},
	pages = {93--104}
}