1. Pedagogic uses of a corpus of student writing
and their implications for sampling and annotation
Alois Heuboeck
University of Reading, UK

2. The British Academic Written English (BAWE) corpus of student writing
Project in progress at the universities of Reading, Warwick and Oxford Brookes
Funded by the Economic and Social Research Council
(project nr. RES )
The BAWE corpus:
corpus of student writing; compiled at R, W & OBU
a specific language corpus: student writing – neither learner nor expert corpus
“experts” of student writing: not the students, but tutors marking
“good quality”
possible progression through levels of studies
under construction; no systematic data available yet
Possible later uses: EAP teaching
Discussed in the following:
usage of corpora in language teaching and their implications on the form of the corpus
what this means for a corpus of student writing; & how decisions on the corpus design try to reflect these requirements

3. Outline Corpora in LT: uses and purposes
Accessing corpus information: interfaces
Building corpora: requirements and decisions - the BAWE corpus

4. Using corpora in language pedagogy
classroom
pedagogic uses
materials
description
“motivational”
purposes
pedagogic uses:
classroom activities: get the student to work with corpus data
data-driven learning, discovery learning
student to fulfil tasks; interpret concordances; find rules himself; formulate new questions and queries;
discover (browse) the corpus; discover language
teaching materials: for the teacher or student; usage in classroom; self study; other learner interfaces (computer-assisted learning, distance learning etc.)
language description: corpus-based grammar and dictionaries (e.g. Biber et al 1999, Longman grammar of spoken and written English, Sinclair 1990/1995, Collins-COBUILD grammar and dictionary), or studies
purposes: why are corpora used?
“motivational” reasons: relating to the student’s (psychological) condition & the process of learning: motivation, autonomy, responsibility
“linguistic” reasons: relating to the object of learning (the “language” as what is being acquired)
- corpus data: “good data”: authentic data; schemata, patterns (lexicogrammar);
repetition and variation in text; new phenomena (not covered by “traditional” grammars)
- advancement and outcome of learning: “good/relevant learning”: task-oriented, user-focused, specific needs
- role of corpus: “reference tool” or “source of communicative tasks” in communicative language teaching (“participatory experience”)
(Aston 1995, “Corpora in language pedagogy” in Cook/Seidlhofer eds.)
“linguistic”

5. Interfaces (1): the concordance
typical query options
word form
lemma
wildcards (e.g. “investigat*”)
Tight relation between information & interface:
information needs an interface to be accessed
grammatical (e.g. POS)
patterns

6. Information & interfaces (2)
Frequencies, ratios
statistics
e.g. word list, key words
ad hoc statistics
macrostructural properties and choices
corpus items
generic types, e.g. CARS model (Swales 1990)

7. Requirements: a “good corpus” for language pedagogy
Representative: target variety
Relevant: information, annotation
Requirements: on corpus design
Validity – representation of a target variety: sampling
Relevance of information accessed: annotation
Practicality
Usable: e.g. interface, size

8. Representativeness Conflicting principles
The corpus as a representative sample should reflect:
Conflicting principles
distribution and quantitative relations
quantitative representativeness
The notion of “representation” is crucial for validity
representation: of a target variety (or a set of target varieties)
2 aspects of representation
=> 2 complementary principles for sampling
range of features
qualitative representativeness

9. Representativeness (2): the BAWE corpus
A trade-off: stratified sampling AH PS
Frame 2: 4 disciplinary groups à 768 ass.
English
History
Linguistics
Classics
Archaeology
History of Art
Physics
Chemistry
Meteorology
Mathematics
Computer Science
Engineering
Frame 1: the university: corpus Σ=3,072 ass.
Frame 3: 4x6 disciplines à 128 ass.
Frame 4: 4 levels per discipline à 32 ass. SS LS
Frame 1: university: i.e. where assgts are written
pb: imbalance across university
Frame 2: disciplinary groupings – the level that reflects most closely the hard vs. soft distinction
pb: => inhomogeneous
=> unpractical for sampling, since the DG/faculty is not the context of production but the...
Frame 3: discipline – the “traditional” approach
for reasons of sampling, “disciplines” are defined in institutional terms as academic Schools/Depts.
disciplines chosen to represent the disciplinary grouping
pb: what is a discipline? – subjects/modules “on the edge” (e.g. “History of science for physicists”, Economics in agriculture);
but also inherently interdisciplinary disciplines, combining courses from several disciplines
(e.g. LS and SS [economy, politics, business administration] in agriculture; Archaeology as AH discipline and “Archaeological science”)
within student’s career: types appearing & disappearing; development in writing
Frame 4: levels of studies
Biological Sciences
Sociology
Law
Business
Health & Social Care
Politics
Anthropology
Publishing
Medicine
Biochemistry
Agriculture
Food Sciences

10. Relevance Relevant information in corpus Significant query
Corpus annotation
Features: lexicogrammatical, structural etc.

11. Relevance (2): features annotated in the BAWE corpus
“grammatical”
textual: structure of “running text”
typographical (lay-out)
metatextual: numbering
grammatical: POS, lemmatisation, “sentences”
textual:
“running text” vs. front/back matter
non-textual elements “interrupting” running text: tables, figures, formulae
chunks of text carrying a particular function which sets them apart from running text: lists
typographical: paragraphs, highlighting, enumerated paragraphs (list-like)
metatextual: numbering of paragraphs and “sentences”
other interesting: e.g. semantic, discursive features
other “interesting” features

12. Modularity: subcorpora
Corpus size
“For the pedagogical analysis of many common grammatical phenomena a full-size research corpus is much too large.” (Osborne 2000)
Modularity: subcorpora
Specialised corpora
“Practical” requirements (mentioned before):
First: interface (related to corpus information) – not dealing with this here
Second: corpus size
Osborne, John, 2000 (p.169): “What can students learn from a corpus?: building bridges between data and explanation”
in Burnard/McEnery eds., Rethinking language pedagogy from a corpus perspective. Papers from the third international conference on teaching and language corpora. Frankfurt/M.: Peter Lang (Łódź studies in language 2)
cf. also: "The use of a few structurally similar texts also enables the identification of some higher-level regularities through concordancing." (Aston 1995: 266)

13. Conclusion: 3 views Qualitative vs. quantitative representation
corpus as representation of a (set of) target variety/varieties
Corpus annotation and interfaces: query
instances of lexicogrammatical (etc.) features and phenomena
Corpus as a whole – representing the target variety
Inside the corpus: “bag of occurrences (of features)”
Corpus as a collection of (potential) subcorpora
Corpus size: modularity
balanced samples of target variety/varieties

14. Pedagogic uses of a corpus of student writing
and their implications for sampling and annotation
Alois Heuboeck
University of Reading, UK
The British Academic Written English corpus