TY  - CHAP
TI  - Natural Language Processing and Learning Analytics
AU  - McNamara, Danielle
AU  - Allen, Laura
AU  - Crossley, Scott
AU  - Dascalu, Mihai
AU  - Perret, Cecile
T2  - The Handbook of Learning Analytics
A2  - Lang, Charles
A2  - Siemens, George
A2  - Wise, Alyssa Friend
A2  - Gaševic, Dragan
AB  - 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.
CY  - Alberta, Canada
DA  - 2017///
PY  - 2017
ET  - 1
SP  - 93
EP  - 104
PB  - Society for Learning Analytics Research (SoLAR)
SN  - 978-0-9952408-0-3
UR  - http://solaresearch.org/hla-17/hla17-chapter1
ER  -