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Comparing expert and learner mathematical language: A corpus linguistics approach

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conference contribution
posted on 08.12.2016, 14:20 by Lara AlcockLara Alcock, Matthew InglisMatthew Inglis, Kristen Lew, Juan P. Mejia-Ramos, Paolo Rago, Christopher J. Sangwin
Corpus linguists attempt to understand language by statistically analyzing large collections of text, known as corpora. We describe the creation of three corpora designed to enable the study of expert and learner mathematical language. Our corpora were formed by collecting and processing three different genres of mathematical texts: mathematical research papers, undergraduate-level textbooks, and undergraduate dissertations. We pay particular attention to the method by which our corpora were created, and present a mechanism by which LaTeX source files can be easily converted to a form suitable for use with corpus analysis software packages. We then compare these three different types of mathematical texts by analyzing their word frequency distributions. We find that undergraduate students write in remarkably similar ways to textbook authors, but that research papers are substantially different. These differences are discussed.



  • Science


  • Mathematics Education Centre

Published in

The XX Annual Conference on Research on Undergraduate Mathematics Education


ALCOCK, L. al., 2017. Comparing expert and learner mathematical language: A corpus linguistics approach. Presented at the Twentieth Annual Conference on Research on Undergraduate Mathematics Education, San Diego, February 23-25th.


Special Interest Group of the Mathematical Association of American on Research in Undergraduate Mathematics Education (SIGMAA on RUME)


AM (Accepted Manuscript)

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