background: english in asia today · general vocabulary, technical terms, phraseology, tense/voice...

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Understanding Writing and Oral Presentation English in Science and Engineering:

A Scientific Analysis

Laurence Anthony Center for English Language Education in Science and Engineering (CELESE),

Waseda University (早稲田大学）, Japan

[email protected]

http://www.antlab.sci.waseda.ac.jp

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Outline

Background English in Asia today

Teaching the English needed in Asia today

Definition of English for Specific Purposes (ESP)

Problems and solutions in ESP for science and engineering

ESP Course Design Needs, Language descriptions, Learning theories

The language of research paper writing

general vocabulary, technical terms, phraseology, tense/voice

The language of oral presentations

vocabulary, style, discourse markers

Applications of language analysis in the classroom Teaching biography writing and presentation Q&A

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Background: English in Asia today

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In 2011, 382 employees took language courses totaling 11,352 training hours.

Taiwan Semiconductor Manufacturing Company Limited

http://www.tsmc.com/english/csr/development.htm

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http://mainichi.jp/photo/archive/news/2010/06/30/20100701k0000m020087000c.html

「2年後に英語が

できない執行役員はみんなクビです」 http://www.toyokeizai.net/business/interview/detail/AC/810ee47297d49033c2a4b43a0a5216e0/page/2/

Tadashi, Yanai, UNIQLO http://www.bus iness-i.jp/news/top-page/topic/200808220004o1.jpg

Hiroshi Mikitani, Rakuten (2010/06/30) http://news.livedoor.com/topics/detail/3578234/

「Any executives who cannot speak English in two

years time will be fired」 http://www.toyokeizai.net/business/interview/detail/AC/810ee47297d49033c2a4b43a0a5216e0/page/2/

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Background: Teaching the English needed in Asia today

Definition of English for Specific Purposes (ESP) (Dudley-Evans, T. & St. John, M. J., 1998: 4-5)

ESP is defined to meet specific needs of the learner;

ESP makes use of the underlying methodology and activities of the discipline it serves;

ESP is centered on the language (grammar, lexis, register), skills, discourse, and genres appropriate to these activities.

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Background: Problems in ESP for science and engineering

Most students' ESP needs are highly specific (see Hyland, 2002, 2004)

e.g. technical writing and presentation skills in STEM disciplines

Developing these skills is resource intensive small class sizes experienced instructors funding time

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Background: A solution to the problems in ESP?

In a traditional English program … give ESP courses elective or

non-credit status introduce strict entry

requirements teach ESP courses only to

interested specialist departments

offer only short-term ESP courses based on external funding

position ESP courses on the fringes of the English program (Anthony, 2009)

Test English (e.g. TOEIC, IELTS, CET,

TOEFL, …)

English for Academic Purposes

Foundation English

English for Discipline Purposes

English for Discipline Purposes

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Background: A real solution to the problems in ESP

Put ESP at the center of program design

integrating all English courses to build ESP skills

combining efforts of English teachers and subject specialists to provide effective ESP experiences

needs analysis

materials design

teaching practices

student evaluation

Test English (e.g. TOEIC, IELTS, CET,

TOEFL, …)

English for Academic Purposes

Foundation English

ESP at the center

Anthony, L. (2009). ESP at the center of program design," in K. Fukui, J. Noguchi, & N. Watanabe (Eds.), Towards ESP bilingualism (in Japanese) (pp. 18-35). Osaka, Japan: Osaka University Press, 2009.

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ESP Course Design: Hutchinson & Waters (1987)

WHAT?

Language Descriptions

HOW?

Learning Theories

WHO? WHERE? WHEN? WHY?

Needs Analysis

ESP COURSE

Syllabus Methodology

Nature of particular target and language situation

ESP Needs Analysis: Who? Where? When? Why?

Necessities: What do the students need to learn to achieve the goal(s) of the course?

Lacks: Which of the necessities do the students lack at present?

Wants: What do the students want to learn?

Factors topics/themes (e.g., science, engineering, business, medicine)

skills (e.g., reading, listening, writing, speaking, fluency)

text types (e.g., research papers, essays, specifications, presentations)

language (e.g., grammar, vocabulary, phrases, pronunciation)

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WHAT?


HOW?

Learning Theories

WHO? WHERE? WHEN? WHY?

Needs Analysis

ESP COURSE



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ESP Language Descriptions: What language should we teach?

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ESP Language Descriptions: What language should we teach?

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vocabulary?

citation patterns?

phraseology?

discourse structure?

discipline variation?

tense/voice?

???

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ESP Language Descriptions: Corpus-informed language analysis

Definition of Corpus Linguistics (Biber, 1998) It is an empirical (experimental) approach

An analysis of actual patterns of use in target texts

It uses a corpus of natural texts as the basis for analysis

Corpus (plural: corpora) = a representative sample of target language stored as an electronic database

It relies on computer software for analysis

Results are generated using automatic and interactive techniques

It depends on both quantitative and qualitative analytical techniques

Observations are counted and results are interpreted

Corpus-informed language analysis Stage 1: Design a corpus

Corpus Development Process

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Review the literature on

language features

Search for pre-built

corpora in target area

Empirical investigation

Choose a target area of language use

Design your own corpus

(DIY)

Decide a sampling

procedure

Search for texts and

save as TEXT

[Annotate the corpus]

Corpus-informed language analysis Stage 1: Design a corpus (random sampling)

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Corpus-informed language analysis Stage 1: Design a corpus (stratified sampling)

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Corpus-informed language analysis Stage 1: Design a corpus (whole population)

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Corpus-informed language analysis Stage 2: Choose a software tool

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International survey of corpus linguists. Reponses: 891. (Tribble, C., 2012)

Tools used to analyze corpora

0% 5% 10% 15% 20%

Other

Xaira (with BNC XML or your own…

Wordsmith Tools

WMatrix

Sketch Engine

Sarah (with BNC)

Oxford Concordancing Program

Monoconc Pro

Longman Mini-concordancer

AntConc

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WordSmith Tools (Scott, M. , 2012) AntConc (Anthony, L., 2012)

COCA (Davies, M., 2012) BNC Web (Hoffmann et al., 2012)


24 AntConc (Anthony, L., 2012)


Freeware

Multiplatform Win 95/98/NT/XP/7

Linux

OS X

Single-file portable app

Unicode compliant

HTML/XML tag handing

Search Features Wildcard/Regex

Tools KWIC Concordancer

Distribution Plot

File View

Clusters/N-grams

Collocates

Word Frequency

Keyword Frequency

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Corpus-informed language analysis Stage 3: Analyze your data!

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Corpus-Informed Language Analysis: The vocabulary of physics research papers

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satellite

image

particle

diagram

???

Corpus-Informed Language Analysis: The vocabulary of physics research papers

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0

20

40

60

80

100

120

140

1 13 25 37 49 61 73 85 97 109

121

133

145

157

169

181

193

205

217

229

241

Fre

qu

ency

Word Ranks in a Research Paper

the

in

a analysis

increase poverty

meteorological

Corpus-Informed Language Analysis: The vocabulary of novels

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0

2000

4000

6000

8000

10000

12000

14000

1 13 25 37 49 61 73 85 9710

912

113

314

515

716

918

119

320

521

722

924

1

Fre

qu

ency

Word Ranks in Harry Potter (Book 5)

Corpus-Informed Language Analysis: The vocabulary of general English

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0

10000

20000

30000

40000

50000

60000

70000

80000

1 13 25 37 49 61 73 85 97

109

121

133

145

157

169

181

193

205

217

229

241

Fre

qu

ency

Word Ranks in the Brown Corpus

Corpus-Informed Language Analysis: The vocabulary of (any) English

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2%

3%

4%

8%

72%

5th 1000 words

4th 1000 words

3rd 1000 words

2nd 1000 words

1st 1000 words

Coverage of Words (in any text)

Based on Brown Corpus

Corpus-Informed Language Analysis: Teaching vocabulary

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"It is important that learners have access to lists of high-frequency and academic words and are able to obtain frequency information from dictionaries." (p. 219)

P. Nation (2001)

"Priority should be given to high-frequency words and to words that clearly fulfill language use needs. (p. 303)

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Corpus-Informed Language Analysis: Technical terms in physics (and other fields)

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satellite

image

particle

diagram

???

Corpus-Informed Language Analysis: Technical terms in physics (and other fields)

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Highest Ranked Keywords in Discipline-Specific Corpora

Rank

1

2

3

4

5

6

7

8

9

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Rank Physics

1 population

2 satellite

3 census

4 data

5 nighttime

6 countries

7 urban

8 dmsp

9 changes

10 ngdc

Rank Physics Math

1 population record

2 satellite system

3 census solution

4 data equations

5 nighttime model

6 countries solutions

7 urban nonlinear

8 dmsp theorem

9 changes equation

10 ngdc stability

Rank Physics Math Biology

1 population record cells

2 satellite system skin

3 census solution cell

4 data equations expression

5 nighttime model mice

6 countries solutions were

7 urban nonlinear protein

8 dmsp theorem induced

9 changes equation keratinocytes

10 ngdc stability tumor

Rank Physics Math Biology Chemistry

1 population record cells reaction

2 satellite system skin solution

3 census solution cell mmol

4 data equations expression mol

5 nighttime model mice bond

6 countries solutions were structure

7 urban nonlinear protein observed

8 dmsp theorem induced spectra

9 changes equation keratinocytes energy

10 ngdc stability tumor complexes

Rank Physics Math Biology Chemistry Comp. Sci.

1 population record cells reaction fault

2 satellite system skin solution cache

3 census solution cell mmol computer

4 data equations expression mol algorithm

5 nighttime model mice bond is

6 countries solutions were structure number

7 urban nonlinear protein observed node

8 dmsp theorem induced spectra systems

9 changes equation keratinocytes energy performance

10 ngdc stability tumor complexes computers

Corpus-Informed Language Analysis: Phraseology in physics (and other fields)

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"… can be seen in …"

"… we found that …"

"… in this paper …"

"… it is important that …"

Corpus-Informed Language Analysis: Phraseology in physics (and other fields)

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Hyland, K. (2008:12). As can be seen: Lexical bundles and disciplinary variation. English for Specific Purposes.

Rank Biology Electrical Engineering

Business studies Applied Linguistics

1 in the presence of

on the other hand

on the other hand on the other hand

2 in the present study

as shown in figure

in the case of at the same time

3 on the other hand

in the case of at the same time in terms of the

4 the end of the

is shown in figure

at the end of on the basis of

5 is one of the

it can be seen

on the basis of in relation to the

Corpus-Informed Language Analysis: The voice of physics (and other fields)

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"We show that …"

"It was shown that …"?

Corpus-Informed Language Analysis: The voice of physics (and other fields)

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Active Form Freq Location Passive Form Freq Location

we found 29 Dis was/were found 26 Res/Dis

we show 20 Dis is/are shown 26 Met/Res

we have 18 Dis is/are had 0 -

we thank 10 Ack is/are thanked 0 -

we observed 15 Dis was/were observed 45 Res/Dis

we used 14 Intro/Meth /Res/Dis

was/were used 78 Met

we investigated 11 Abs/Int/ Res/Dis

was/were investigated 6 Abs/Int/ Res/Dis

Dermatology Corpus (52 texts, 89246 tokens)

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ESP Language Descriptions (Presentations): What language should we teach?

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vocabulary?

hedging?

phraseology?

Q&A?

style?

???

http://drtomcrick.files.wordpress.com/2011/04/feynman.gif?w=612&h=465 0

2000

4000

6000

8000

10000

12000

you so

i is

we

tha

t

this t

he

re

goin

g s

no

w it

min

us re

ma

trix m

zero

wh

at x if l

see

can

do r

tim

es

let

tha

vect

or

mit

plu

s

curr

en

t

ok v b

fie

ld

be

cau

se

ma

gne

tic

eq

ual

s

the

n

wan

t ll

om

ega

hav

e

get

eq

uat

ion

just q

char

ge

Corpus-Informed Language Analysis: Keywords in technical presentations (MIT OCW)

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you

I

going see vector

equation

so

here

magnetic

Corpus-Informed Language Analysis: The use of "slide", "I", and "you" in presentations

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Usage of "i" Usage of "you" Usage of "slide"

Corpus-Informed Language Analysis: Introducing the next slide in presentations

Common phrases using "slide" "… on the next slide I show you …"

"… and the next slide is a bubble chamber …"

"… the next slide shows you a corona discharge …"

"… the next slide shows you something similar …"

"… the next slide shows you the tunnel …"

Common phrases using "show" "… I showed you a picture …"

"… I have just shown you …"

"… I'll show you here …"

"… I'll show you that …"

"… Let me show you a case where …"

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Corpus-Informed Language Analysis: Presentation style

Research paper style (not good in presentations) Results

Western Blot Analysis

Using Western blot analysis, GC-17 was demonstrated to specifically recognize two commercially available recombinant ER-s proteins and show no cross-reactivity to an ER-{alpha} recombinant protein (Figure 3; A, B, and C ).

The recombinant short-form ER-s protein (RP311) from Affinity Bioreagents Inc. has an estimated molecular size of 53 kDa and represents a polypeptide (corresponding to amino acid residues 43 to 530) translated from the second initiation codon of the ER-ß transcript.

http://a jp.amjpathol.org/cgi/content/full/159/1/79 45

Corpus-Informed Language Analysis: Presentation style

Better ... Results

Let me now show you the results

Western Blot Analysis

First, I’ll describe the Western Blot Analysis.

Using Western blot analysis, GC-17 was demonstrated to specifically recognize two commercially available recombinant ER-s proteins and show no cross-reactivity to an ER-{alpha} recombinant protein (Figure 3; A, B, and C )..

What we found was that GC-17 recognized two commercially available recombinant ER-s proteins.

It also showed no cross-reactivity to an ER-{alpha} recombinant protein.

You can see this here.

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Corpus-Informed Language Analysis: Hedging in presentations

Hedging in presentations ("I" - strong possibility) "… I can almost predict when it happens …"

"… I can always do that …"

"… so now I can predict that …"

"… It can actually be much better …"

Hedging in presentations ("We" - account of previous actions) "… we could change L in the circuit …"

"… we could solve this …"

Hedging in presentations ("You - abstract example") "… you could also think of it as …"

"… you could imagine that running out …"

Hedging in presentations ("It - hypothetical case ") "… it may be like this …"

"… it may not melt at all …" 46 47


WHAT?


HOW?

Learning Theories

WHO? WHY? WHERE? WHEN?

Needs Analysis

ESP COURSE



ESP Learning Theories: How should students learn language?

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"Numerous studies now show the extent to which language features are specific to particular disciplines, and that the best way to prepare students for their studies is not to search for universally appropriate teaching items, but to provide them with an understanding of the features of the discourses they will encounter in their particular courses.."

(Hyland, 2008: 20)

ESP Learning Theories: How should students learn language?

The language of specialist subjects is highly variable (Hyland, 2002; Hyland, 2004; Hyland and Bondi, 2006; Paltridge, 2009; Biber, 1992; Lea, 1996)

But, a student's specialist area is very likely to change through his or her career

Students need to know about probabilistic variation in core language elements (Anthony, 2012)

Students need to understand .... what features vary, how features vary, when features vary

Students need to .... recognize, analyze, and estimate probabilistic variation in

language features across texts and genres

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The ESP Specificity Continuum Dudley Evans & St. John (1998), Anthony (2012)

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ESP Methodology

Teacher Centered classroom organizer:

Initiation: teacher Response: student Follow-up: teacher

Learner Centered classroom consultant:

Initiation: student Response: teacher Follow-up: student

ESP Content

General ESP (e.g., academic listening, note-

taking, logical structures, visualizing data)

Specific ESP (e.g., nuclear physics

terminology, reactor safety manuals)

Narrow ESP (e.g., research article writing,

presentations)

Corpus-Informed ESP Classroom Practices: Learning about probabilistic variation

How can ESP teachers help students understand what, how and when language features vary in and across different disciplines (and genres)?

How can ESP teachers empower students to be able to identify what, how and when language features vary in future (unseen) texts?

Introduce Data-Driven Learning (DDL) into the ESP classroom

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Characteristics of Data Driven Learning (DDL): A focus on the exploitation of authentic materials

A focus on real, exploratory tasks and activities

A focus on learner-centered activities

A focus on the use and exploitation of tools

52 (Bernd Rüschoff, 2010)


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AntConc (Anthony, L., 2012)

GoTagger 0.7 (Goto, K., 2006)

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Example: Teaching Biographies writing

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57


57

10


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"A short biography and passport-style photograph of every author should be provided for Survey Papers, Papers and Brief Papers when requested by the Editor."

Elsevier Automatica http://www.elsevier.com/wps/find/journaldescription.cws_home/270/authorinstructions

"Include in the manuscript a short (maximum 50 words) biography of each author"

Elsevier Pattern Recognition http://www.elsevier.com/wps/find/journaldescription.cws_home/328/authorinstructions

"Biographies should be brief."

IEEE Canada http://www.ieee.ca/journal/authorinfo_style.html

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Part 1: Using the AntConc File View Tool, quickly scan through your corpus and note down the information given in the first sentence of the biographies.

What do you notice?

Part 2: What information is usually given in the second sentence of the biographies?

Is the second sentence giving more information about the topic in the first sentence ?

Is the second sentence describing something different?

Part 3: What other information is given in the biographies?

List the most common information that the authors describe

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Laurence Anthony received the M.A. degree in TESL/TEFL, and the Ph.D. in applied linguistics from the University of Birmingham, Birmingham, U.K., and the B.Sc. degree in mathematical physics from the University of Manchester Institute of Science and Technology (UMIST), Manchester, UK. He is a Professor in the Faculty of Science and Engineering at Waseda University, Tokyo, Japan. His primary research interests are in educational technology, corpus linguistics, and natural language processing.

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Laurence Anthony received the M.A. degree in TESL/TEFL, and the Ph.D. in applied linguistics from the University of Birmingham, Birmingham, U.K., and the B.Sc. degree in mathematical physics from the University of Manchester Institute of Science and Technology (UMIST), Manchester, UK. He is a Professor in the Faculty of Science and Engineering at Waseda University, Tokyo, Japan. His primary research interests are in educational technology, corpus linguistics, and natural language processing.

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Example: Dealing with Presentation Q&A Sessions

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Why is Q&A so difficult?

“The questioner had really poor English and so couldn’t understand what he was saying.”

“The questioner was really confusing. She kept changing the words of her question, and repeating herself.”

“The questioner didn’t ask a question at all. He just made a comment and that was it.”

“The audience was really hostile. They were just trying to find problems in my work.”

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Typical questions asked by a presentation audience Background

What are you doing? What is your aim? What problem are you trying to solve?

Body What is the meaning/value of your data? How/Why did you decide your method? What does a word/number/table/chart mean?

Discussion How can the results be applied? How will you continue your research from now?

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Understanding the question Questions are not usually direct.

“Why did you perform that experiment?”

Questions follow the pattern …

Frame → Issue → Request

“At the beginning of the presentation, …”

“... you showed a photograph of the proposed robot.”

“Could you tell me how big it was?”

(“Did you explain how big it was?”)

Frame → Issue → Comment

“At the beginning of the presentation, …”

“... you showed a photograph of the proposed robot.”

“It looks very small.”

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Typical questions asked by an Asian audience WH: How big was it? (50%)

Comment: I think this is quite big. (20%)

Indirect: Could you tell me how big it was? (10%)

YN: Was is big? (10%)

A or B: Was it big or small? (10%)

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Typical questions asked by an Native English audience Indirect: Could you tell me how big it was? (31%)

YN: Was is big? (24%)

WH: How big was it? (21%)

Comment: I think this is quite big. (14%)

A or B: Was it big or small? (3%)

Negative: Wasn’t it big? (3%)

Rhetorical: How big is it? (3%)

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0

10

20

30

40

50

60

Indirect YN WH Comment A or B Negative Rhetorical

Asian

Native English

%

Conclusions

English for Specific Purposes (ESP) is a useful approach for learning the language needed by scientists and engineers It is based on real student needs

It is based on analyses of actual language usage

Corpus tools are being increasingly used by both teachers and students of science and engineering They provide teachers and students with ways to

identify the language of specialist disciplines

They empower teachers and students to answer questions about specialized English without needing native-speaker support

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background: english in asia today · general vocabulary, technical terms, phraseology, tense/voice...

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