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Tasks for Business Science and Technology English:
Evaluating Corpus-driven Data for ESP
University of Extremadura
Reading performance is closely related to writing in English for Specific Purposes (ESP) courses. These skills can be compared, in fact, at the micro-level of language learning. Namely, concordancing activities prove to be effective for academic reading abilities and writing achievement (cf. Johns, 1991; Thurstun and Candlin, 1998).However, even though language behavior occurs at the syntagmatic plane, and, this factor, if exploited conveniently, can aid specific discourse writing considerably (cf. Pueyo and Val, 1996), I believe that more feedback is needed from the scope of specific language learning tasks.
For instance, in a 'constructive task for genre awareness-raising´ (Jordan, 1997: 169), appropriate academic readings trigger the learners recognition of core lexico-grammatical elements `to assimilate conventions´ of genres (Jordan, 1997: 169), but, in our experience, this pattern building is not realized unless actual communicative input is produced in specific tasks.
The general purpose of this paper is to describe the powerful learning effect of corpus-based tasks for specific settings, where academic discourse priorities alternate with technical concerns.Interaction of special terminology with academic writing generally provides benefits for knowledge in the subject area and academic performance. The language is analyzed as the exponent of contextual factors; these aspects are the discipline-based concepts and existent academic community conventions managed by students (cf. Starfield, 2001). Both describe our specific setting in ESP, where Business and Computer English are important components of English for Academic Purposes (EAP) and English for Professional Purposes (EPP).
ESP programs are therefore devised as task-based syllabi. Corpora are designed to develop written activities that strengthen reading and writing skills in the subject area. However, not until the evaluation of task performance can the ESP teacher actually confirm the validity of such corpus-driven material for his/her courses. In this regard, ESP syllabi result from continuous learning processes (cf. Hutchinson and Waters, 1987).
The distribution of words is examined in a preliminary lexical analysis of my Business Science and Technology corpus. Lexical phrases and collocations are considered key formations in this text collection. They contribute to developing the linguistic framework of academic communication. In this sense, the overall aim is to fulfill the global context of academic writing, where lexical standards are set. Coxhead and Nation (2001), for instance, analyze and update the importance of academic vocabulary for tertiary settings.
In the context of Business and Computer English, I examine items that occur across eight genres used by Business and Computer students --textbooks, news, reviews, reports, electronic discussions, conference papers, abstracts, and research articles. Some lexical results are: data, market, information, management, financial, analysis, etc. These are significant, highly frequent corpus-driven collocations --send + data, view + data, job + market, stock + market, sales + information, information on the web.
Gaining great recognition and acceptance by the academic community, the lexical items above serve as basic material for the EAP side of Business and Computer English. Nonetheless, target groups in our setting also disclose specific language needs for reading and writing technically, which are considered chief requirements of specialized publications.
In the learning context, there is a much too wide fissure between the proper linguistic choice and the production of content. In other words, on several occasions, technical output is criticized on the sole basis of language deficiency (e.g., non-native speakers of English in international conferences). From the beginning, the consistent investigation of linguistic structures should therefore aim to production instead of mere reception.
My view is based on the study of lexical patterns according to contextual variables. Specialized language is thus examined within the `eco-system´ of tertiary level curricula (Holliday and Cookes, 1982), where lexical bonds are both technical and academic. Language is seen in terms of scientific-technical discourse features (Trimble, 1985), and as academic traits (Jordan, 1997; Dudley-Evans and St. Johns, 1998).
Target material in the corpus corresponds to readings and lectures in the academic setting. The data can give insightful information on the authentic use of language for either common core academic levels, or for more specialized purposes within the subject area. Patterns of word behavior are generally identified in academic texts (cf. Hoey, 1991), and, as a result, these texts are conceived as units in discourse-based or rhetorical activities (cf. Trimble, 1985).
These specific sources are classified according to subject interests. An example is the area of Software engineering, where research articles are written for advanced students in Computer Science. Language variation is thus expected according to subject matter complexity. Lexical distinction corresponds to three different degrees of word use (Curado Fuentes, 2001): 1, academic, 2, rhetorical-grammatical, at a more basic plane, and 3, thematic language as technical vocabulary.
Selected according to prominence in the texts, academic items are semi-technical collocations that include significant noun, verb, adjective and adverb combinations. These are common core in our academic area of Business Science and Technology, given their high frequency and dispersion rates. Rhetorical grammatical combinations result, instead, from critical colligations, i.e., grammatical words activating certain content items. Such combinations lead to marked rhetorical functions in discourse, varying according to subject matter focus. Finally, thematic elements are considered technical collocations in the subjects.
In this framework, rhetorical functions are derived from critical grammatical combinations. In the Business and Computer Science context, these are measured across general academic readings and lectures. In order of frequency, the most common functions are arranged as follows: Discussions, Descriptions, Classifications, Definitions, Exemplifications, and Conclusions. They include a total of 43,546 tokens or running words, and 1,232 types or distinct vocabulary.
The top 100 mainly involve features of `signposting´ in discourse (cf. Winter, 1977). The first five are by, on, section, use, such, which correspond not only to grammar words but also procedural elements. By semantic prosody observation, discourse values are properly labeled at the syntagmatic stage. An example is reproduced in Table 1 for the case of the number one grammar item, by.
Table 1:Distinctive use of by in rhetorical contexts
Even though significant differences exist among the sections,uniformity is likewise inferred. For instance, in Classifications and Exemplifications, the passive structure with by abounds. In contrast, the unit `by + verb-ing´ denotes instrumentalization (in Definitions and Discussions). In the case of Descriptions, the utilization of by means of is significant as a rhetorical marker. Classifications present their own semantic peculiarity in by title.
By means of critical statistics in the form of M.I. (Mutual Information) scores, degrees of collocational probability are assessed (cf. Ooi, 1998). In this line of work, patterns reflect priority linguistic concerns according to stylistic shift in the corpus.
Academic and semi-technical items
The second major division of our material refers to the type of interaction between reader and writer, or audience and speaker, in academic settings: How objectives and purposes are expressed, and how strongly ideas and notions are conveyed (cf. Bhatia, 1998). To underline the formation of these events, in fact, the majority of the central lexical items correspond to content words, such as nouns and verbs that clearly classify as semi- or sub-technical elements: `notions general to all or most of the subject areas´ (Yang, 1986: 98).
Some of the most frequent words found are data, market, information, number, provide, digital, and available (from a 2,000-word list in our corpus Detailed Consistency List (DCL), which includes the eight genres aforementioned: textbooks, reviews, reports, news articles, e-discussions, conference papers, abstracts and journal articles). Adjectives and adverbs are also frequent. The DCL is an academic wordlist, in agreement with Coxhead and Nation (2001).
Table 2 presents academic collocations with the noun information.
Table 2:Typical academic collocations in the corpus
Subject and technical material
Nominal compounds tend to behave as key elements in specialized or technical discourse (eg journal articles). For instance, in the subject of Client server Communications, which is an important field in the area of Business technology, the noun applications combines with specific collocates to refer to technical concepts in networks:
Table 3:Key items in subject category
The expressions are identified as descriptors of the subject area. They can also be checked in a specialized database including the domain of Business Science and Technology: the Computer Literature Index Quick Subject Locator. Key Words in Context (KWIC) are thus revised in this reference material, which contains technical papers from which terminology is derived.
A contrastive view of the noun applications can be given by examining another technical field, that of 'Automated knowledge-based applications' in the Business Science and Technology corpus. Technical compounds are checked, in this case manifesting specific computer-related language:
Table 4:Key items in subject category
CORPUS-DRIVEN DATA AND ACADEMIC TASKS
As mentioned before, the focus on grammatical and lexical collocations leads to teaching encoding skills: `The introduction of a system of syntactic patterns, (...) and the use of a controlled vocabulary´ (Cowie, 1988: 688) foster ESP learning. In addition, the perspective of the task-based approach leads to the teaching of 'language for the subject specialism´, and of `tasks based on the specialized content´ (Edwards, 1996: 13).
None of these issues can be ignored in the process of working with a corpus for task exploitation in the ESP course. As a Business and Computer English instructor, I perceive that the more I know and get involved with the subject area, the more valuable my guide and instruction can be considered by the academic community. This recognition includes language revision of research papers and articles for peers as well as subject matter collaborations in funded projects.
Examples of activities and tasks parallel the three lexical levels in my corpus -- academic, rhetorical and technical. However, these activities are subject to change by having to adapt to the learners' needs (i.e., their specific linguistic demands).
Academic wordlists and concordance lines
Writing skills can be motivated through the exploitation of resources such as academic wordlists and concordances. Up-to-date corpus material works as preliminary reference data. At this stage, providing learners with prioritized lexical combinations can be fruitful in terms of 'linguistic preferences'. Academic wordlists offer learners the possibility to infer suitable linguistic constructions for academic writing.
For instance, the verbs call and define are significant in the definition of concepts. The former call -- is even more typical --in structures like this is called a and method which we call a. This stylistic preference can be exploited in the ESP classroom by means of writing exercises where learners are encouraged to define basic concepts of General Business technology, e.g., e-commerce, e-firms, or e-businesses. The output obtained from the compositions can be contrasted with specific concordance lines, such as the following (Table 5):
Table 5:Concordance lines exemplifying uses of the verb call in academic discourse
The concordance lines are selected carefully to illustrate common core academic use, which is made in different academic genres, thus aiming for a basic language in the subject area of Business Science and Technology. In Table 5, students are provided with examples from management reports (lines 1 and 3), Law conference papers (line 2), and Law research articles (line 4). Learners' impressions vary according to their level of academic competence. In other words, as seekers of concise academic writing, they should favor common core expressions.
This is called is thus the structure chosen by most students when revising written compositions. The task includes the goal of peer reviewing. Individually and by groups, effectiveness is sought by these objective and productive means.
In addition, some learners convey their preference for I call this as the common core structure of concept definitions. We call this was also listed by some, although, in this case, these were very advanced students who must write a short research paper for Economics (a five-year major in our institution). Interestingly enough, some of our colleagues, subject instructors who were tested on this particular linguistic concern, favored the expressions we call this and we define this as, and, yet, they blindly accepted the corpus-based passive sentence this is called as a better option for future writing.
In contrast, the verbs organize and arrange illustrate different learning situations. The first one is very common in my academic wordlist, and the resulting patterns organize + content, sections, topics, lessons are common core, occurring significantly across texts. In turn, the verb arrange is typical in research texts, where characteristic structures such as arrange + elements, structures, nodes and lines are noteworthy. Confirmed by subject instructors, arrange is frequently used in explanatory writing to classify items, albeit in a more research-oriented environment.
Sign-posting is perceived as critical ESP material in academic discourse. A main trait of sign-posting is procedural language in the form of markers that are also recognized by many learners in my courses. How a notion is expressed, a given procedure or concept explained, or product described, involves coming to terms with signposting in discourse. An example is a simple structure such as used by title (see Table 1), denoting classification.
A lexical analysis of rhetorical functions demonstrates that some procedural marker is always included in the paragraphs. Such marking devices can be revised by means of collocational charts, where corpus-based fill-in the gap exercises foster cognition. In addition, these activities 'are easy to prepare and students have a sense of familiarity with this format' (Thurstun and Candlin, 1998: 273). Students operate with informational tables (see Table 6) to check for demanded connectors in discourse.
Table 6:Fill-in-the gap chart with rhetorical marker
Depending on the level of language command, students are told or not about the particular type of discourse function to be fulfilled: constrast, reason, etc. In this case, it is the formulation of causes or reasons, and the main colligation to identify is because of, which co-occurs notably with the noun demand in the corpus (shown in all the lines of Table 6). This type of explicit activities tends to assist learners' development with cohesion knowledge at intermediate levels of study (2nd and 3rd years in Business and Computer English). Nonetheless, advanced students and even some of my colleagues are eager to also exploit such rhetorical items in order to improve their writing command.
Collocational charts are also helpful in the acquisition of content word combinations. Main words or lemmas are examined in their typical academic expressions. For instance, the adjectives that go with the noun program are existing and current. The first is more common, according to my corpus data, and yet, more students complete the charts with current than with existing. In this case, as a result, corpus-based information serves to contradict preferences, and to instruct learners about typical vs. less common use.
Technical collocates also provide effective input. Complex noun compounds are characteristic in specialized discourse, where the tendency is that the longer the technical collocation, the higher the degree of expertise. This corpus-driven assumption is corroborated by subject instructors.
Generally, in terms of receptive skills, learners seem to do well on the technical input. However, they lack the ability to produce effective technical language in the form of collocations, given the opposite order of such structures in Spanish and their difficulty to be interpreted without a solid subject background. For instance, communication applications and interprocessor communication applications (Table 3) are often written as applications for communications and applications for communications of interprocessors, even by some subject teachers.
Technical vocabulary should therefore be exploited by means of corpus-based charts. An example is Table 7, where the central collocate (management) is to be guessed by discerning specific collocates placed on the left and right of the node.
Table 7:Collocational chart of technical item
Features of academic genres become important contextual elements in language learning tasks. A central distinction lies, in this scope, between the type of specific teaching and that with a more general focus on the academic setting. Edwards (1996: 2) claims that external factors such as the kind of format in which the document is to be presented, or the type of revision and dissemination made of such a work, play decisive roles in ESP to distinguish ESAP (English for Specific Academic Purposes) from EGAP (English for General Academic Purposes) tasks.
In this study, contextual dimensions demand the target group of learners in the academic community to conform to precise norms of style and content. Communicative skills in the genres significantly aid the learning process. For example, reports are quite common, frequently read and taken from the internet. The consequence is that a more informal tone is met in recurrent clauses such as we are interested in, we are happy to conclude that, we are dealing here with, etc. In contrast, academic research in conference papers and articles calls for a higher degree of formality, as is stated in phrases such as we define this as, we have described the, or we will apply the.
Lexical items characterize some academic text types and not others. In the genre of textbooks, for instance, the key words found are significantly different from those of news articles: in the textbook, for example, the pronoun you is distinctive, whereas he is a key word in news articles. Table 8 depicts this and other variations.
Table 8:Genre-based lexical variation
A practical manner of appreciating the value of genre-based language for ESP learning is the specific task (ESAP). Many exercises that test the students learning abilities encourage their overall comprehension / production skills, such as skimming, scanning, anticipating, note-making, planning, organizing, outlining, note-taking, etc. Nunan (1989) refers to these are primary techniques for problem analysis, vocabulary exploitation, discussion, note-taking, etc in EAP (Nunan, 1989: 122). In addition, for the proposed purpose of specific genre-based learning, task specification must contain the necessary interaction of language and content.
Some examples are examined qualitatively through specific oral report presentations given by students. This approach is similar to Warschauer's proposal (2000) to evaluate language learning in specific settings: An ethnographic examination of learners' output, developed in authentic context-based, purpose-oriented tasks.
The intra-learning process analysis (Hutchinson and Waters, 1987: 62; Robinson, 1991: 14; Nunan and Lamb, 1996: 34) serves as rich literature on which to base the exploration and application of findings related to genres in EAP and ESP. The issue involves the question of how learners learn. Thus, the goal in this particular task is to check present learners' impressions about authentic language use for oral tasks: the oral exposition of a topic studied or researched in their undergraduate courses of Business Science and Technology.
Table 9 summarizes the main results obtained in two technical English courses: second year of Business Science at the Faculty of Business, University of Extremadura, and third year of Computer Science students at this institution. These are the learners' comments on language vs. content delivery problems:
Table 9:Learners' comments about importance of items in oral presentations
The importance of specific vocabulary in relation with subject matter is corroborated by 96 percent of learners. However, academic items are regarded as more relevant than technical elements. Students perceive this difference by contrasting subject-specific or thematic words (e.g. automated management knowledge) with common core lexis in the discipline (e.g. management data). Nonetheless, even more important are constructions evolving from particular grammar words (e.g. by providing management with). These usually trigger rhetorical functions, and yet, when undergraduate learners are asked about the use of discourse markers (e.g., because of -- see Table 6), they do not judge them as very important for their oral presentations -- in contrast with graduate students and subject instructors, who regard them as crucial.
The texts favored in oral reports are professional, as can be observed in Table 9. Visual aids to illustrate the content of these genres are of foremost importance, according to my learners. A descriptive register, influenced by the reading of news articles, was developed in most presentations. Structures like the ones examined in Table 8 -- he said that, he added that, he commented on-- were, indeed, widely used by presenters, especially in Business English. Little interaction was made, in contrast, between speaker and audience, with very few cases of the use of second person pronouns. This agrees with the fact that few students made use of textbooks as reference material for their presentations.
Undergraduate learners thus tend to place more emphasis on genres related to the future workplace. They are more interested in business reports and readings with technology product information (e.g. description of sales technology, goods, etc). In contrast, advanced students whose concerns include reading and writing research, prefer academic approaches in which technical articles and papers are used.
The implications of this data for the Business Science and Technology English corpus design point to the enhancement of both academic and professional planes. Genres such as reports, reviews and news, related to the vocational side of Business (descriptions and functions of companies, technology, etc), must be stressed. Academic sources, (e.g. textbooks, research articles, abstracts, e-discussions, conference papers) must also be considered, but they should not overwhelm undergraduate learners.
In terms of lexical preferences, the focus on academic / common core language is made clear. This inclination should be fulfilled by the inclusion of different subjects and genres, so that there is ample evidence for building a general language within a specific setting: Basic academic / professional vocabulary in the Business Science and Technology domain.
In turn, the use of technical items is not as highly considered. The general tendency is for learners to already have a suitable knowledge of subject matter, which leads them to memorize these items. Therefore, there should be fewer advanced readings in the corpus. However, for writing skills, technical vocabulary activities must be maintained, thus meeting some of the advanced learners' needs.
The main purpose of this paper has been to demonstrate the importance of task evaluation in Business and Computer English for the common domain of Business Science and Technology. The relationship between specific corpus-driven language and the manner of producing such a language is analyzed in the academic setting. The analysis of lexical items is thus subject to how effective the data proves to be for the learning situation.
My specific corpus of Business Science and Technology English comprises eight different genres with which the academic community is familiar. The texts include three main levels of lexical behavior: 1.rhetorical, 2.common core / semi-technical / academic, and 3.technical. These divisions stem from corpus-based lexical analysis and personal research. Because of the applied nature of the investigation to actual reading and teaching material in class, my lexical approach contributes sound data on which to base the design of tasks and activities. Some relevant activities include the assistance of academic word lists and concordance for writing purposes, or collocational charts for filling-in-the-gap exercises that aim to strengthen both academic and technical production skills.
In addition, genre distinction is made in the case of oral tasks. When delivering an oral report, learners expressively demonstrate their actual needs and lacks in the use of academic language. Undergraduate students perceive their strong demand of specific vocabulary that is semi-technical and common core in the subject area; in addition, they point to the use of pivotal grammatical combinations and some discourse markers that signal the structure of discourse. However, discourse markers are not often observed as noteworthy by undergraduate learners.
The production of technical words and compounds is seen as quite important in writing, given the opposite order of Spanish nominal constructions. In addition, subject instructors and graduate students give high priority to the use of discourse markers for research writing. In fact, advanced language users tend to claim the importance of the academic vocabulary of Business and Computer English for their personal research. In contrast, undergraduate students prefer the use of professional discourse, as their comments on genre (Table 9) show.
Business Science and Technology English should contain both academic and professional discourses. The eight genres selected can be classified as either academic (e.g. textbooks, research articles, abstracts, e-discussions, conference papers) or professional (e.g. reports, reviews, news). In addition, common core language is favored to express concepts and ideas; the consequence is that a wide diversity of general topics and genres must be maintained in the corpus. Technical items, in contrast, should be stressed less. In the inclusion of specific topics in the corpus, complex texts should be avoided or at least kept to a minimum.
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