Language learning theories underpinning corpus-based pedagogy #cl2015


Lynne Flowerdew
Language learning theories underpinning corpus-based pedagogy

The noticing hypothesis (Schmidt)

Attention consciously drawn

Noticing linked to frequency counts

Implicit vs explicit learning

 Constructivist learning

Learners engage in discovery learning

Inductive learning

Cognitive skills, problem solving to understand new data

Widmann et al. 2011: the more possible starting points for exploitation, the more likely for different learners- SACODEYL project.

Sociocultural theory

What about language learning outside the classroom and incidental learning?


Learner corpus research plenary #cl2015

Learner corpus research: a fast-growing interdisciplinary field

Sylviane Granger



LCR IS an interdisciplinary research

Design: learner and taks variables to control

Not only English language

Method: CIA (Granger, 1996) and computer-aided error analysis

Wider spectrum of linguistic analysis

Interpretation: focus on transfer but this is changing; growing integration of SLA theory

Applications: few up-and-running resources but great potential

Version 3 (2016 or 2017) around 30 L1s as opposed to 11 L1s in Version 1

Learner corpora is a powerful heuristic resource

Corpus techniques make it possible to uncover new dimensions of learner language and lead to the formulation of new research questions: the L2 phrasicon (word combinations).

Prof. Granger brings up Leech’s preface to Learner English on Computer (1998)

Gradual change from mute corpora to sound aligned corpora

POS tagging has improved so much

Error-tagging: wide range of error tagging systems: multi-layer annotation systems

Parsing of learner data (90% accuracy Geertzen et al. 2014)

Static learner corpora vs monito corpora

CMC learner corpus (Marchand 2015)

Granger (2009) paper on the learner research field:

Granger, Sylviane. “The contribution of learner corpora to second language acquisition and foreign language teaching.” Corpora and language teaching 33 (2009): 13.


CIA V2 Granger (2015): a new model

SLA researchers are more interested in corpus data and corpus linguists are more familiar with SLA grounding

Implications are much more numerous than applications

Links with NLP: spell and gramar checking, learner feedback, native language id, etc.

Multiple perspectives on the same resource: richer insights and more powerful tools


Louvain English for Academic Purposes Dictionary (LEAD)


corpus based

descriptions of cross-disciplinary academic vocabulary

1200 lexical times around 18 functions (contrast, illustrate, quote, refer, etc.)

A really exciting application









MA of L2 learner English

Corpus Linguistics 2015, University of Lancaster, 21-24 July


Yu Yuan:
“Exploring the variation in world Learner Englishes: A multidimensional analysis of L2 written corpora”

109 features included in the analysis


Can Biber’s model be extended?

How do features co-occur in learner English?



ICLE 1.0 (Granger, 2002)

SWEECL 2.0 (Wen & Wang, 2008)



MA tagger Nini (2014) Manual here. Software (Windows) here.

Stanford Corenlp


Pythin scripts



Kaisser’s criteria + Scree test for Factor Analysis



10 dimensions stand out

Dimensions are largely epistemological, rhetorical and syntactical.