Technology in Mathematics and Science Distance Education: Automated Textual Analysis of Articles and Proceedings Papers using Leximancer

Year 2018, Volume: 9 , 28 - 35, 18.08.2018

David Thomas

Abstract

This paper
presents an analysis of 30 recent journal articles and proceedings papers
addressing the use of technology in mathematics and science distance education.
The analysis is performed using Leximancer (2017), an automated textual
analytics tool. The study asks, 1) “Which concepts occur most frequently
relative to each discipline?”; 2) “How do frequent concepts vary between the
disciplines?”; 3) “Which themes emerge as most characteristic of this
discourse”; and “What do the disciplinary
document sets have in common?”. The
findings offer strong evidence in support of a conjecture that discourse
associated with the use of technology in distance education is conducted by
mathematics and science education scholars using systematically different
concepts and themes to represent their
interests, methods, and findings.

Keywords

Technology, Distance, Mathematics, Science, Education

References

• AACE (2017). Association for the Advancement of Computing in Education. Waynesville, NC 28786 USA. www.aace.org Beeferman, D., Berger, A., & Lafferty, J. (1997, July). A model of lexical attraction and repulsion. In Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics (pp. 373-380). Association for Computational Linguistics. Learning and Technology Library (2017). Association for the Advancement of Computing in Education (AACE).www.learntechlib.org/ Leximancer (2017) (Version 4.0). Leximancer Pty Ltd, Brisbane, Australia. [Software] Available from https://info.leximancer.com/ Leydesdorff, L., & Hellsten, I. (2006). Measuring the meaning of words in contexts: An automated analysis of controversies about 'Monarch butterflies, ''Frankenfoods,' and 'stem cells'. Scientometrics, 67(2), 231-258. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological review, 84(3), 231. Smith, A. E., & Humphreys, M. S. (2006). Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping. Behavior research methods, 38(2), 262-279. Thomas, D. 2014. Searching for significance in unstructured data: Text mining with Leximancer. European Education Research Journal, 13(2), 2014. Zimitat, C. (2006). A lexical analysis of 1995, 2000 and 2005 Ascilite conference papers. In Proceedings of the 23rd Annual Ascilite Conference: Who’s learning (pp. 947-951).