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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
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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Background: English in Asia today
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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Background: English in Asia today
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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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
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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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4
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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Corpus-informed language analysis Stage 2: Choose a software tool
WordSmith Tools (Scott, M. , 2012) AntConc (Anthony, L., 2012)
COCA (Davies, M., 2012) BNC Web (Hoffmann et al., 2012)
Corpus-informed language analysis Stage 2: Choose a software tool
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Corpus-informed language analysis Stage 2: Choose a software tool
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
10
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
ESP Course Design: Hutchinson & Waters (1987)
WHAT?
Language Descriptions
HOW?
Learning Theories
WHO? WHY? WHERE? WHEN?
Needs Analysis
ESP COURSE
Syllabus Methodology
Nature of particular target and language situation
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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Corpus-Informed ESP Classroom Practices: Learning about probabilistic variation
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
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Corpus-Informed ESP Classroom Practices: Learning about probabilistic variation
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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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Example: Teaching Biographies writing
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Example: Teaching Biographies writing
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Example: Teaching Biographies writing
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Example: Teaching Biographies writing
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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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Example: Teaching Biographies writing
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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Example: Teaching Biographies writing
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: Teaching Biographies writing
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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Example: Dealing with Presentation Q&A Sessions
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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Example: Dealing with Presentation Q&A Sessions
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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Example: Dealing with Presentation Q&A Sessions
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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Example: Dealing with Presentation Q&A Sessions
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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Example: Dealing with Presentation Q&A Sessions
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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Example: Dealing with Presentation Q&A Sessions
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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