Individual Differences among Student Teachers in Taking an Online Corpus Linguistics Course: A Multiple Case Study

Individual Differences among Student Teachers in Taking an Online Corpus Linguistics Course: A Multiple Case Study

Alice Ebrahimi (Alzahra University, Iran) and S. Susan Marandi (Alzahra University, Iran)
DOI: 10.4018/978-1-4666-8519-2.ch004
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Recently, educating teachers in computer- assisted language learning (CALL) has gained much popularity. In this regard, corpus linguistics (CL), as an area related to CALL, has received great attention. Researchers, now argue strongly for the inclusion of CL in language teacher education (LTE) programs. However, there is no research on how individual and contextual differences may affect student teachers' (STs) reactions to such training. This chapter reports on a qualitative study which explored a range of variables related to STs' personal and professional backgrounds as well as group dynamics influential in the adoption and application of CL training by STs. Through analyzing the data collected using surveys, interviews, and students' written evaluations of the course, it was found that STs' years of teaching experience, characteristics and beliefs, prior experience of online communication, access to technology, and familiarity with and attitudes toward CALL play a crucial role in this regard.
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Continued advances in educational technology have profoundly changed the way teacher education programs are being offered. Teacher educators today have unlimited opportunities to more broadly utilize and employ powerful technological tools, to equip teacher candidates with the skills, knowledge, motivation and support needed to integrate the power of technology into their classrooms and instruction. Indeed, the influence of technology in teacher education programs is so great that, it is said, it has changed the “way teachers teach, and learn to teach” (Elliott, 2009, p. 433).

In this respect, an area of interest within language teacher education (LTE) programs has been educating teachers across the field of computer-assisted language learning (CALL). Over the last few decades, the so called “CALL teacher education” (Hubbard & Levy, 2006) has gained much popularity among teacher educators. In fact, in some parts of the world, teacher educators are obliged to incorporate CALL into their training courses, and language teachers, by the same token, are required to use computer technologies in their classes. As stated by Hubbard (2008), teachers’ technological expertise is considered advantageous skills for recruitment in some teaching English as a second language (TESL) career centers.

However, an important concern still remains for teacher educators: what technical and pedagogical training in CALL is needed for language teachers? Some technologies such as e-mails, wikis, blogs, podcasts, etc. have been addressed widely; nevertheless, others have not yet been considered as positive and welcome addition to educational settings.

As an example, mention can be made of a relatively neglected technology in LTE in Iran, i.e. corpus linguistics (CL). According to McEnery, Xiao and Tono (2006), CL is “a whole system of methods and principles of how to apply corpora in language studies and teaching/learning” (p. 7). Nowadays with the prevalence of personal computers and easy access to the Internet, it becomes increasingly convenient to search for words – both written and spoken −in a large collection of language data in electronic form, by means of computerized text analysis tools called concordancers. Concordancers are powerful software programs – either installed on a computer or accessed online – that display all the occurrences of a word with surrounding contexts, provide frequencies of words and clusters, analyze collocates, and often offer statistical information on the strength of word associations.

Recently, many experts in the field of LTE have acknowledged that concordancing could benefit teachers in developing their competencies in several ways. For example, some have claimed that corpus analysis empowers teachers to gain greater awareness of the target language i.e. awareness of the use of lexical items, collocation patterns and language structures, which in turn, would lead to much greater awareness of the teaching content and use of course book materials (Tsui, 2004). Once trained in the basic corpus analysis procedures, teachers can exercise their autonomy by developing their own teaching materials and resources (Jarsolaw, 2009; McCarthy, 2008).

Another contribution of corpora could be promoting teachers’ critical awareness by enabling them to examine the contents of dictionaries and textbooks against corpus data. Also, teachers can quench their professional curiosity by compiling their own corpora (either from learners’ artifacts, textbooks or the Internet) and enhance their “research skills” and reflection ability (O’Keeffe & Farr, 2003, p. 389). On the whole, corpus-based research has the potential to serve as a “teacher development tool” (Vaughan, 2010, p. 472).

Considering the potential benefits of corpora in language learning and teaching, researchers in the field now argue strongly for the inclusion of CL either as a core/elective subject in LTE programs for pre-service teachers or as a professional development course for in-service teachers (Breyer, 2009; Gabrielatos, 2005; Gilquin & Granger, 2010; Granath, 2009; McCarthy, 2008; McKay, 2009; Mukherjee, 2006; O’Keeffe & Farr, 2003; Römer, 2009).

Key Terms in this Chapter

Fully Online Courses: Courses that are delivered entirely over the Internet and do not require face-to­ face communication between students and instructors.

Hybrid Courses: Courses which integrate face-to-face and online activities in a way that fewer than 20% of the course activities are carried out online.

Discussion Boards: Asynchronous software programs which allow the participants to post messages and reply to others’ posts in written texts. They are also known as discussion forums, electronic bulletin boards, and conference areas.

Individual Differences: Differences among people as a result of their behaviors, attitudes, traits and personality characteristics, and circumstances.

Computer Assisted Language Learning (CALL): An approach to language teaching and learning in which computers are used as an aid to language instruction.

Corpus: A corpus (plural corpora) is a collection of electronically stored texts from written or spoken language which is representative of a genre.

Corpus Linguistics: Refers to the study of language using computer-assisted techniques and software to analyze large body of linguistic data which are sampled to be representative of a language variety.

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