Handbook of Learning Analytics
Chapter 11
Modeling Educational Discourse with Natural Language Processing
Nia Dowell & Vitomir Kovanović
Abstract
The broadening adoption of technology enhanced learning environments has substantially altered the manner in which educational communication takes place, with most people engaging in some form of online asynchronous or synchronous conversation every day. The language and discourse artifacts emerging from these technological environments is a rich source of information into learning processes and outcomes. This chapter describes the current landscape of natural language processing (NLP) tools and approaches available to researchers and practitioners to computationally discern patterns in large quantities of text-based conversations that take place across a variety of educational technology platforms. The capabilities of NLP are particularly important as, in the field of learning analytics, we desire to effectively and efficiently learn about the process of learning by observing learners, and then subsequently use that information to improve learning. We conclude the chapter with a discussion around the emerging applications (i.e., sensing technologies, breakthroughs in AI, and cloud computing) and challenges of NLP tools to educational discourse.
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No Supplementary Material Available
Title
Modeling Educational Discourse with Natural Language Processing
Book Title
Handbook of Learning Analytics
Pages
pp. 105-119
Copyright
2022
DOI
10.18608/hla22.011
ISBN
978-0-9952408-3-4
Publisher
Society for Learning Analytics Research
Authors
Nia Dowell
Vitomir Kovanović
Editors
Charles Lang
Alyssa Friend Wise
Agathe Merceron
Dragan Gašević
George Siemens