The Application of Corpus Tools in the Teaching of Discipline-Specific Academic Vocabulary: A Case Study for Information Engineering Undergraduates

The Application of Corpus Tools in the Teaching of Discipline-Specific Academic Vocabulary: A Case Study for Information Engineering Undergraduates

Min Zhang (Faculty of Foreign Studies, Beijing Information Science and Technology University, Beijing, China)
DOI: 10.4018/ijcallt.2013100104
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Abstract

This article concerns a corpus-based lexical study that is aimed at teaching academic vocabulary to address the specific needs of Chinese undergraduates majoring in information engineering. A 1,024,882-word corpus of Information Engineering English Corpus (IEEC) was built on the basis of university-level textbook materials drawn from ten compulsory courses for information engineering students. A quantitative analysis was carried out to seek for an optimal frequency threshold for extracting frequently occurring academic vocabulary specific to the discipline of information engineering based on Coxhead’s Academic Word List (2000). An innovative 2-dimensional categorization of AWL for EAP students was adopted in the study. As a result, a 100-word highly frequent core AWL and a 147-word frequent AWL were compiled for IEEC under two parameters of lexical frequency and specificity to individual sub-corpora. The present study further explored the application of corpus tools to highlight and effectively teach the discipline-specific academic vocabulary and collocations to promote learners’ autonomy and enhance their lexical competence in the study of specialized courses.
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The Academic Word List (AWL) is a list of 570 word families (head words) which covers 3107 types (individual word forms including the headwords and their inflectional and derivative family members) and is grouped into 10 sublists that are developed by using a 3.5 million-word written academic corpus covering four discipline areas of arts, commerce, law and science (Coxhead, 2011, p. 355). Data from Brown Corpus show that a reader needs to know another 3000 words to raise the coverage of texts from 79.7% (the first 2000 words from GSL) to 88.6% (Nation, 2001, p. 15). The AWL is ground-breaking in that upon mastering the GSL words, university students can expect to reach comparable text coverage and know about 90% of the running words they will meet in any academic text if they embed another 570 academic words into their lexical repertoire. A number of studies have shown that the coverage of AWL words in academic texts is consistently around 10% in both multi-disciplinary corpus and discipline-specific corpus (Coxhead, 2011). They make it possible for EAP learners to enhance vocabulary recognition and reading abilities notably “for a relatively modest learning investment” (Cobb, n.d.).

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