Linguistic Analysis of Science Teachers' Narratives Using AntConc Software

Linguistic Analysis of Science Teachers' Narratives Using AntConc Software

Yulia Muchnik-Rozanov (Technion - Israel Institute of Technology, Israel) and Dina Tsybulsky (Technion - Israel Institute of Technology, Israel)
Copyright: © 2020 |Pages: 20
DOI: 10.4018/978-1-7998-1173-2.ch010
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This chapter presents the linguistic analysis of science teachers' narratives regarding their worldviews in the digital age and their views of technology. The analysis was performed using Laurence Anthony's software AntConc, which is suitable for analyzing large data corpora. The language behavior of the following groups of teachers was analyzed by exploring three distinctive linguistic markers: personal pronouns to study participants' foci of attention; emotion words, to measure the extent of their emotional immersion in the discourse; and semantic fields of specific word collocations. The results, based on the variations in the language behavior, indicated differences between the three groups of teachers' worldviews. In addition, the examination of the degree of descriptive elaboration, expressed through the use of sense, motion, and exclusion words, revealed similar levels of truthfulness in all three groups. The linguistic analysis, enhanced by various computational linguistic technologies available through the AntConc software, made it possible to identify implicitly conveyed thoughts and feelings, thereby affording a better understanding of complex education-related processes and phenomena.
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The connection between the human mind and language has fascinated scientists from various fields since the concept of “Freudian slips” was first introduced over a century ago – a Freudian slip is a linguistic behavior which unintentionally reveals an individual’s true thoughts and feelings (Freud, 1901; Tausczik & Pennebaker, 2010). Since then, the value of analysis of language (linguistic analysis) has been widely acknowledged in psychology (e.g., Freda, Esposito, & Quaranta, 2015; Gottschalk & Gleser, 1969; Weintraub, 1989), psychiatry (e.g., Bersudsky Fine, Gorjaltsan, Chen, & Walters, 2005; Fine, 2006; Nienow & Docherty, 2004; Smirnova et al., 2014; Smirnova et al., 2015; Stirling et al., 2006), and forensic sciences (e.g., McMenamin, 2002; Sudjana & Fitri, 2013). However, in educational research its significance and affordances are still underestimated. Although studies in education often rely on content analysis methods, the specific analytic practices used are often insufficient for delving deeply into complicated educational phenomena. Specifically, studies are often conducted without employing computational linguistics technologies (Ursúa & Vásquez, 2008), thereby limiting the sample size and making the inquiry more time-consuming. Only a limited number of studies have addressed this issue directly (Tsybulsky & Muchnik-Rozanov, 2019; Ursúa & Vásquez, 2008).

The digital age, with its huge technological advances and emerging technologies, has created new possibilities for integrating linguistic analysis into educational research. With the emergence of computational linguistics technologies, such as the Linguistics Inquiry and Word Count (LIWC) software programs (Pennebaker, Booth, & Francis, 2007; Tausczik & Pennebaker, 2010), researchers can computationally assess the effect of certain linguistic features found throughout large text corpora. For analyzing non-English corpora, however, LIWC dictionaries need to be especially designed, which adds to the complexity of analyzing data in relatively rare languages or in languages with graphically unique characters. An example is the development of the Chinese LIWC dictionary by the National Taiwan University of Science and Technology in 2007 (Li, Cai, Graesser, & Duan, 2012). In other words, if the data obtained need to be translated from the participants’ mother tongue into English (or into any other globally used language), the undeniable advantage of analyzing writers’ and speakers’ authentic language samples is rendered null and void. The AntConc program, created by Laurence Anthony, a professor in the Faculty of Science and Engineering at Waseda University, Japan, successfully solves this problem.

Key Terms in this Chapter

Semantic Field: A set of lexemes which together denote a certain segment of reality, cover a respective conceptual domain, and bear a certain relation to one another.

Digital Era: A period in human history characterized by the shift from traditional industry to an economy based on information and communications technology (ICT).

Computational Linguistic Technologies: Computer software created to optimize the linguistic analysis of large text corpora.

Level of Immersion: The extent to which a narrator/speaker is emotionally involved in the process or event described in the narrative.

Systemic Functional Linguistics (SFL): A field of linguistics that views language as a system of options to be employed for the realization of different social functions in the social context.

Focus of Attention: The person or object which is the main concern of one’s narrative.

Linguistic Analysis: The analysis that takes language phenomena as the chief focus of study.

Digital Society: A society corresponding to the Digital Era in human history. This is an ICT-dependent society, where the creation, distribution, use, integration, and manipulation of information become the main economic, political, and cultural activities.

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