Name

Teun De Rycker

Affiliation

Lessius Hogeschool

Departement Handelswetenschappen

Korte Nieuwstraat 33

2000 Antwerpen

Belgium

Email address

Title

A Multi-Skill Strategy for Helping Students Communicate Numerical Information

A Multi-Skill Strategy for Helping Students Communicate Numerical Information

Teun De Rycker, Lessius Hogeschool

1. Introduction

In the field of English for Academic Purposes (EAP) a lot of attention is rightly paid to silent reading comprehension (e.g., Zukowski-Faust 2002) and essay writing (e.g., Thurstun & Candlin 1997; Hyland 2000). However, non-native university and college students also need to become proficient in analyzing numerical information in English, that is, information that has been presented in the form of tables, graphs and charts. To quote Fisher (1990:91), “[s]tudents often have problems with numeracy skills and in seeing the integration between text and diagram. Native speakers may, in fact, have similar problems.” — an observation also made by Hayes (2000) when examining number-narrative linkage in business writing. Equally importantly, students should be able to communicate the outcome of their analysis accurately and to blend both data and findings successfully into presentations, academic articles, reports, and other forms of oral or written communication. Yet, as Jordan (1997:204) points out, “verbalizing data” is an area of considerable difficulty for many students in higher education and one that needs to be addressed through careful and repeated practice.

In this paper I would first like show how a deceptively simple four-step worksheet can help learners hone their data-analytical skills and simultaneously expand their linguistic repertoire for communicating “numbers” not only accurately but also fluently and with confidence. As such, it is primarily meant for EAP but also business English instructors in higher education who wish to improve their learners’ reading, writing and speaking skills in the context of visual inputs. Secondly, I will also examine to what extent a selection of classroom activities that build on the four-step data analysis worksheet (DA worksheet) can help instructors achieve these outcomes in a task-based language teaching and learning (TBL) way. The assumption is that only by using the worksheet to develop genuine “tasks” rather than mere “activities” or “exercises” — as these terms have been differentiated by Ellis (2003:2-16) but see, for example, Littlewood (2005) for a different approach — will instructors and learners alike stand to benefit the most from adopting it.

2. The Data Analysis worksheet

As I reported elsewhere (De Rycker 2001), one of the reasons why students in their role as “statistical consumers” (Helberg 1995) tend to misinterpret data is that they often tend to do “too little, too fast.” After all, tables, graphs and charts are a means to a cognitive and/or communicative end like solving a problem or holding a presentation — and are of scant value as an end in their own right. As a consequence, students are frequently tempted to comb through the data with a view to finding what they are looking for (e.g., support for a claim, justification for a particular decision or a fitting illustration of a trend) instead of looking for what can be found.

To address this problem of “selective visual attention” and to try and improve my students’ overall numeracy skills I have developed a worksheet that guides them through a chronological sequence of four easy-to-remember activities, which — like street bumps — slow them down in what is often nothing else than a fast-paced search for quantitative evidence. Each of these four steps has been given a label ending in “–ation” to ensure maximum memorability: orientation, generalization, explanation, and exploration. Each of these steps is aimed at spotlighting a common pitfall: failure to look at the building blocks of the table, graph or chart in question; failure to grasp the main issues; failure to distinguish between description and explanation; and failure to separate facts from opinions. Before examining what role the DA worksheet may play in a TBL approach to academic reading and writing, let me briefly clarify what is meant by these four terms (for more details, see De Rycker 2001).

2.1. Orientation

Before getting down to interpreting the quantitative information, students will first have to get their bearings by checking off the following seven points.

Type of data / What type of data presentation has been chosen (e.g., a pie chart)? And why? Answering this double question will help foreground the possibilities and limitations of a particular table or diagram priorto analysis.
Movie or snapshot? / Does the data presentation include a time scale or not — a feature that we refer to by means of the terms “movie” and “snapshot”? It is important to check this in advance as both come with their own types of generalizations and repertoires of lexical expressions and grammatical constructions.
Title / If there is one, which type is it, e.g., a generic title, an eye-catching title or an action statement? And what does it tell us about the visual?
Source / Where does the table, graph or chart come from? And where did it get the data from itself? What do these sources tell us about potential for bias, reliability, etc.?
Labels / This involves having a close look at axis and line labels in graphs, row and column headings in tables, and legends that go with bar or pie charts.
Unit(s) of measurement / Are we talking yen, light trucks or barrels of crude oil? Do the data represent actual figures, forecasts, projections, etc.? Do we have percentages, coefficients or index numbers?
Definitions / Look up the meaning of the labels and other pieces of text. Some students start interpreting the numbers without fully understanding what they are about. In this respect, eye-catching chart titles often obscure more than they inform (e.g., “April Tax Showers”) as readers will have to figure out their metaphorical meanings.

Table 1: Checklist for the orientation stage of the Data Analysis worksheet

See also Saunders, Lewis & Thornhill (2003:338) for a similar but more simplified checklist for diagrams and tables.

2.2. Generalization

The next step in the analytical process is to give an accurate and complete description of the data and to reduce this to a small number of maximally useful generalizations. It is all about spotting patterns, identifying trends, and discovering similarities and differences. What the DA worksheet is meant to do here is address three persistent problem areas: incomplete and random descriptions (“the trees rather than the wood”); muddled thinking; and vagueness or inaccuracy in reporting the findings in English.

To enhance the quality of the analysis, my approach is to make worksheet users divide their descriptive generalizations into what I refer to as “extremes” and “in-betweens” (De Rycker 2001:74). For “movies” (see 2.1 above), the “extremes” are (1) the starting and ending points of the time period covered, (2) the highest and lowest data points during this period, and (3) the largest and smallest changes — including significant tipping points. The “in-betweens” refer to anything else like minor fluctuations, speed of change or comparisons across consecutive years within the period. For “snapshots,” the “extremes” refer to the largest and smallest data categories (e.g., wedges of a pie chart), and the “in-betweens” to anything that lies in between. Finally, students are also required to number every observation. This numbered list of descriptive statements — in either the “extreme” or “in-between” category — forms a better basis for the next stage in the analysis than continuous prose.

As for the other two problem areas, EAP instructors should be especially wary of hasty generalizations, leaving important information out of consideration, and other inductive fallacies that undermine the validity of an analysis. Students should also be able to report their interpretations in an informative way, using an appropriate range of clear, factual linguistic choices and combinations.

But these are not the only areas for improvement. When I began experimenting with “data-to-text”/“text-to-data” communication tasks, I was amazed to find how often reasons, assumptions, opinions, judgments, expectations, and even emotions like anger and joy snuck their way into this part of the analysis. Apparently, the task of writing a purely factual description conflicts, at some deeper level, with our roles as “compulsive” sense-makers and human beings with likes and dislikes, hopes and fears. I realized that the worksheet had to include space for explanations — and also for personal reactions, which brings us to the next two sections of the worksheet.

2.3. Explanation

Explanation (or answering the question “Why?”) is indubitably the most challenging stage in the entire process of scientific discovery. As with generalization, logical reasoning skills are central to analytical success. But there is also a second problem, viz., “adhoccery”. This refers to the fact that students hardly ever explain their findings systematically, let alone, apply the same range of causal possibilities. Their data reports often read as random collections of quantitative observations and half explanations. Generalizations, observations, and explanations are not always kept sufficiently apart. In fact, most of the explaining done occurs on a case-by-case basis so that almost every phenomenon gets treated differently.

Unlike muddled thinking, adhoccery is largely a matter of language competence, and especially, of content generation and subsequent textual organization. To introduce more consistency, balance, and structure, I have developed two tactics. First, as I already observed above (see 2.2), all generalizations and other descriptive statements have to be numbered. When students explain the data, I make them copy the number(s) of the observation(s) that they explain. In this way, it will become immediately apparent which statements have been explained, and which ones have not — something that is much harder to ascertain when both activities are integrated into one running composition. This approach helps students work more systematically.

The second tactic is to make students misspell “explanation” as “ECSPlanation,” in which the four capitalized letters stand for respectively “economy,” “culture,” “society,” and “politics.” What I would like them to remember is that it may not be enough to write down one economic reason for statement No. 1 and one cultural reason for statement No. 4. Though it may not always be possible or relevant, it is important to treat every statistical finding in the same way.

Explanation is by far the most difficult step in the worksheet, and in fact, may take a long time with lots of additional research. For this reason, I do not really expect my EAP or business English students to always answer the “Why?” question in detail. The level of completeness required will depend on the intended audience, the communicative purpose, and other variables in the writing situation.

2.4. Exploration

The worksheet is not only meant to make students look at the data in a hard, cold-steel, factual way. It also offers them an opportunity to do what often comes most naturally, namely, to respond to the numerical information, to form certain judgments, to reflect on them. This is a point also made by Saunders, Lewis & Thornhill (2003:422-423), who observe that “[f]indings presented without reflective thought run the risk of your reader asking so what?”

Most students are tempted, as I observed above, to express their own views and opinions through, for example, evaluative statements like “The unemployment rate is too high” instead of its more descriptive counterpart “The unemployment rate is 12%.” If they are going to evaluate anyway, we might as well make them do so, but then, in a place where it will not interfere with the more non-judgmental part of the data analysis. Thus, in the last box of the worksheet, students are free to offer evaluations, to indulge in speculation, to discuss possible implications, to bring in other facts and figures, to tell us what they found surprising or interesting, to examine the way the data affect them as human beings or students — in short, to explore the table, graph or chart from their own highly subjective perspective.

3. A task-based approach to teaching and exploiting the Data Analysis worksheet

In this section I would like to examine to what extent filling in the DA worksheet itself can be looked upon as a genuine “task,” and if not, how it can perhaps be exploited in order to generate more authentic communication and/or task-like activities on the part of the worksheet users.

3.1. Lexical and grammatical focus

Before starting, however, it is useful to briefly list the lexical items and grammatical patterns that are relevant to completing the four worksheet boxes but which — in line with accepted TBL thinking — may well be introduced at a later stage. As Willis & Willis (2004) observe,

a focus on specific grammar rules or patterns will not generally come before the task itself, as this could detract from the real communication purpose of the subsequent interaction.

What is more,

Instead of beginning with the grammar we would begin by teaching words and phrases relevant to the topic of the task set and asking learners to carry out tasks which encourage learners to make the best use they can of these.

For each of the four stages discussed in the previous section, it is possible to “pre-teach” words, lexical phrases and expressions that are generic to the task of analyzing data presentations, i.e., regardless of what these data are specifically about, and to raise our students’ awareness of, and sharpen their skill at producing, a number of syntactic forms. For my own groups of business and economics students, most of whom are intermediate to advanced learners of English, it is usually sufficient, in my experience, to focus on the following:

Orientation / Words for the different kinds of data visualization (e.g., “multiple bar chart”) and their characteristics (e.g., “X-axis,” “to stack,” or “solid line”). Conventions for writing numbers (e.g., the use of commas to separate thousands). The use of the simple present tense in definitions.
Generalization / Different ways of lexicalizing change including its direction, amount and speed (e.g., “to bottom out” or “a dip”). The use of prepositions in verbal phrases like “to fall by 10%”. A review of the tenses like simple past and present perfect — an indispensable resource in reporting “extremes” and “in-betweens.” A review of how adjectives and adverbs are used to compare and contrast data (e.g., “grow more rapidly than”).
Explanation / Subclauses of reason. Discourse markers.
Exploration / The language of emotions and reactions. Focusing constructions like the cleft sentence (e.g., “What I found rather interesting was …”). Discourse markers.

Table 2: Focus on lexis and grammar in the Data Analysis worksheet

Note that in the last two sections of the worksheet students are required to write full texts rather than individual sentences or short paragraphs as in the generalization stage. Hence, the focus on discourse markers, coherence, and other features of textual organization.

3.2. Teaching the worksheet: Task or activity?

Naturally, the decision as to how much to teach in advance and how much to leave to an inductive “sink or swim” methodology depends on student proficiency, the amount of time available, the overall course objectives, and teaching style and preferences. As Long (2005:13) puts it, pedagogic procedures “comprise the potentially infinite range of local options for realizing the [methodological] principles at classroom level.” Actually, I prefer to discuss, illustrate, and practice the four worksheet stages two by two, building on what students know from other courses (e.g., visual communication or inferential statistics) as well as their own experience in processing and discussing data in a foreign language. However, this present-practice-produce procedure is not the only “local option,” and as a matter of fact, there is no reason why students cannot be given a table, graph or chart and asked to complete the worksheet on their own. The question then is whether doing so would constitute a task.

According to Ellis’s definition (2003:16), there are seven criterial features for judging whether an activity, i.c., filling out the DA worksheet, is a task or not. In what follows, I will briefly apply these criteria to our four-step approach.

(1) Is the DA worksheet a workplan? Yes, it is. The worksheet specifies exactly what the student is supposed to do at every stage in the analysis.

(2) Does the DA worksheet involve a primary focus on meaning? This is another “yes.” The emphasis is on using language pragmatically with a view to communicating one’s understanding of a table, graph or chart. The main speech act category in a Searlean sense would be that of world-to-word (or rather visual-to-word) representatives: reporting, informing, stating, suggesting, claiming, etc. The information gap involved lies between the visual and its interpretation as made explicit through some linguistic output (e.g., general trends, salient classes, tipping points, incongruities, etc.). What the DA worksheet does is enable a learner to close that gap in a systematic way.

(3) Do the participants choose the linguistic and non-linguistic resources to complete the task? This question is harder to answer. On the one hand, the worksheet itself does not “force” students to use certain pre-specified forms. It describes the sequence of subtasks, which in combination with the visual, will create a particular semantic space (e.g., “pay differentials between men and women in managerial positions in the US automotive industry between 1995 and 2005”). However, this only “constrains what linguistic forms learners need to use, while allowing them the final choice” (Ellis 2003:10). On the other hand, it is obvious that the four DA stages predispose students to using specific words or structures like those listed in Table 2. Even so, as with all tasks, whether meant to be form-focused or not, the final decision rests with the learner — and he or she “can always use communication strategies to get round using the targeted feature” (Ellis 2003:17).

(4) Does the DA worksheet involve real-worldprocesses of language use? As I argued elsewhere (De Rycker 2001:76), completing the worksheet has no fidelity as a written communication genre and may, at best, be only a “prewriting problem-solving plan” (Winter 1996). So, the answer here is negative. The worksheet can never be the real-world end product but only a step towards something else like an essay or presentation. As a preparatory document, it has many benefits, however. It requires students to organize their ducks in a row; helps them generate critical insights and potentially useful information; and may reduce the kind of stress that non-visual, less number-minded learners experience when confronted with heaps of undigested numerical information.