Pre-university, undergraduate and postgraduate vocabulary
Like Medicine, Engineering has been an area of research into specialised vocabulary and the majority of this research is based on corpora. Several studies have used student textbooks as a corpus for identifying specialised vocabulary to support student learning. Chapter 2 outlined a study (Ward, 2009) which discussed the development of a Basic Engineering List for university students in Thailand. This exercise created a fairly powerful list of 299 words (such as equation, process, show and temperature) which covered 11.3% of the Engineering textbook corpus. Another study of Engineering was carried out by Mudraya (2006), who developed an Engineering Academic Word List made up of 1200 word families based on a corpus of 12 textbooks from 13 disciplines, which totalled nearly two million running words. This study also focused on learners in Thailand and identified 1260 word families for students to focus on in their studies, including items such as all, force, form, give, part, point, show, problem and work.
A study based in Taiwan by Hsu (2014) used a textbook corpus (4.57 million running words; 100 textbooks; 20 subject areas) to develop an Engineering English Word List (EEWL). The first 2,000 words of English were excluded from this study, and other selection principles included range, frequency and uniformity. A total of 729 word families met the selection criteria, and covered 14.3% of the corpus. Hsu (2014) identified lexical items that occurred in some parts of the corpus but not others. For example, membrane and enzyme are high frequency items in Biomedical, Biochemical, Biotechnology and Marine Engineering but do not often occur in Communication, Mechanical and Civil Engineering.
In a recent paper, Watson-Todd (2017, p. 35) focuses on specialised vocabulary in Engineering through a corpus analysis which involves both quantitative and qualitative approaches. The first steps involve identifying Engineering vocabulary in a corpus, and the next steps involve investigating the ’opaqueness’ of these words. ’Opaque’ words are defined as ’words which do not have their usual meaning’. This concept is discussed in Chapter 2 in the section on everyday words as specialised vocabulary. Watson-Todd’s research took place in Thailand and focused on the English vocabulary needed by Engineering students. His corpus contained 27 textbooks (approximately 1.15 million words). Initial steps for identifying technical vocabulary were a keyword analysis with the Engineering corpus and the BNC, and applying selection criteria such as removing abbreviations. These first steps garnered a list of 186 candidates for the opaque analysis. The overlap between this list and the Ward list was 47.31%; and with the first 100 word families from Mudraya’s list was 39.78%. The top ten words in the 186-word list from Watson-Todd (2017, p. 38) are determine, flow, figure, temperature, energy, force(s), pressure, function(s), equation(s) and shown.
For the opaque analysis, Watson-Todd (2017) used the corpus to compare the meanings of 186 words in context with the meanings in online dictionaries which were commonly used by students. Using six selection criteria for selecting candidates for his word list, including parts of speech and meaning checks with the Engineering corpus, the BNC and several dictionaries, Watson-Todd (2017) identifies items such as constant (n) an Engineering word as having a high opaque rating because it has a specialised meaning of ’fixed number’. The meaning-based analysis is illustrated well in the contrast between note (n) with a low opaque rating with the meaning of ’brief record’ and note (n) with a high opaque meaning of ’notice’. Table 6.6 contains examples of words and their opacity ratings from Watson-Todd (2017). Note that this table also includes collocations to the left and right of the target word, and examples from the corpus. The final list of opaque Engineering words contains 41 items which are less general and more oriented towards Engineering (Watson-Todd, 2017, p. 37).
Table 6.6 Examples from Watson-Todd’s (2017, p. 36) opacity-ranked Engineering word list