Customising elearning tools to learner needs: the contribution of learner corpora

(1) Brief description of the pedagogical context:

One of the main difficulties that foreign language learning tools need cope with is the high degree of variability that characterizes the learning process. For one thing, language learners come in many shapes and forms. They display a wide range of proficiency levels and even within one and the same proficiency band, experience different difficulties depending on a range of variables, notably their mother tongue background. In addition, learners have different needs in terms of skills (exclusively or primarily receptive – reading/listening - or productive – writing/speaking) and domain (general or specialized). While CALL programs distinguish between proficiency levels and skills, the variables of mother tongue background (learners’ L1) and domain specificity are generally disregarded. Rundell’s (2007) criticism of ‘the current globally-marketed one-size-fits-all package’ that has hitherto prevailed in pedagogical lexicography could in fact be levelled at the majority of language learning resources, including CALL.

Customizing language learning resources to learner needs requires detailed information on the types of difficulties faced by learners, in particular L1-related difficulties. This type of information can be obtained from two types of learner data: (a) learner corpora like the International Corpus of Learner English (Granger et al 2009), which are collected by researchers to inform SLA theory and/or pedagogical resources, or (b) dynamic data sets, which are collected on a continuous basis via web-based language learning environments (cf. Wible et al 2001) or computer-mediated communication (Belz & Vyatkina 2008).

Customization in terms of domain can be achieved by careful analysis of discipline-specific corpora (medical corpus, business corpus, biology corpus, etc.) and automatic integration of specialized patterns of use and examples into learning resources, as well as access to focused exercises and discipline-specific corpora.

In my presentation I will describe the ICLE and show how we are using it, together with other learner corpora and a wide range of native corpora, to inform ELT tools, focusing more particularly on a web-based dictionary designed to help learners write academic texts in English.

(2) Brief description of the tool:

LEAD: Louvain English for Academic Purposes Dictionary
Web-based dictionary-cum-CALL (Abel 2010)

MySQL database; written in PhP (with some JavaScript)

Main characteristics:

- productive tool: designed to help learners write academic texts in English; focus on the phraseology of academic words (collocations and recurrent phrases); entirely corpus-based (both native and learner corpora)

- customized in terms of the learner’s L1 (currently French)

* L1-specific error notes

* L1-specific exercises

* L1-specific access (via the translation in the learner’s mother tongue)

- customized in terms of the learner’s discipline (currently medicine, business and linguistics)

* Discipline-specific examples

* Direct access to discipline-specific corpora

- multiplicity of access modes:

* Via the meaning (onomasiological)

* Via the lexeme (both single words and multi-word units) (semasiological)

* Via the translation

(At this stage, the prototype is only available on the intranet of Louvain university)

(3) Brief description of the corpus sample:

Four argumentative essays extracted from the International Corpus of Learner English (version 2) database (Granger et al 2009): 2 from Spanish-speaking learners and 2 from French-speaking learners.

Three different formats:

1) raw texts

Texte - 13.9 kb

2) versions of the texts part-of-speech tagged with CLAWS7 (cf.

Texte - 119.3 kb

3) versions of the texts as they are integrated into the ICLE CD-ROM (conversion from CLAWS7 to Unitex-compliant format + addition of simplified tags) (cf.

Texte - 50.8 kb

(4) Brief description of the pending research issues related to the tool:

1) Integrating the resource into wider language learning environments

2) Compiling a corpus of learner writing in a range of disciplines (cf. VESPA project:

3) Automating learners’ L1 identification (cf. JojoWong & Dras (2009)

Return to the gerenal programme: EUROCALL 2010, workshop "Dissemination and comparison of research findings: developing Contextualized Learning and Teaching Corpora (LETEC)"


Abel, A. (2010). Towards a systematic classification framework for dictionaries and CALL. In Granger, S. & M. Paquot (eds.) eLexicography in the 21st century: New challenges, new applications. Proceedings of ELEX2009. Cahiers du CENTAL N°7, 3-11. Louvain-la-Neuve, Presses universitaires de Louvain.

Belz, J. A. & Vyatkina, N. (2008). The pedagogical mediation of a developmental learner corpus for classroom-based language instruction. Language Learning and Technology, 12/3, pp. 33–52.

Granger, S., Dagneaux, E., Meunier, F., Paquot, M. (eds.) (2009). The International Corpus of Learner English. Handbook and CD-ROM. Version 2. Louvain-la-Neuve: Presses universitaires de Louvain.

JojoWong, S-M & Dras, M. (2009). Contrastive Analysis and Native Language Identification. Proceedings of the Australasian Language Technology Association Workshop, 53-61, Sydney.

Rundell, M. (2007). The dictionary of the future. In Granger S. (ed.) (2007). Optimizing the role of language in Technology-Enhanced Learning. Proceedings of the Integrated Digital Language Learning (IDILL) expert workshop organized within the framework of the EU-funded network of excellence Kaleidoscope, Louvain-la-Neuve (Belgium), 4-5 October 2007.

Wible, D., Kuo, C.-H., Chien F.-Y., Liu A. and Tsao N.-L. (2001). A web-based EFL writing environment: integrating information for learners, teachers, and researchers. Computers and Education, 37: 297-315.

titre documents joints

1 August 2010
info document : PDF
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