Corpora for Theory and Practice

1996 ◽  
Vol 1 (1) ◽  
pp. 1-37 ◽  
Author(s):  
Michael Barlow
Keyword(s):  
Second Language ◽  
Language Learning ◽  
Text Analysis ◽  
Second Language Learning ◽  
Theory And Practice ◽  
Corpus Analysis ◽  
Data Driven ◽  
Actual Usage ◽  
Grammatical Analysis ◽  
Patterns Of Use

In this paper intuition-based studies of reflexive forms such as myself are contrasted with a corpus-based investigation of actual usage of reflexives. The examination of reflexives in English in several corpora reveals a variety of patterns, which are analysed within a schema-based approach to grammar (Barlow and Kemmer 1994). This approach follows the cognitive/functional tradition of grammatical analysis in viewing all grammatical units as composed of form-meaning pairings. The paper demonstrates that a schema-based approach is well-suited to the task of describing the major and minor patterns of use revealed by corpus analysis. The importance of text analysis in language teaching is highlighted and connections between the schema-based grammatical formalism and data-driven approaches to second language learning (Johns 1991b) are briefly explored.

Technology-mediated task-based language teaching: A research agenda

2020 ◽  
pp. 1-17
Author(s):  
Bryan Smith ◽  
Marta González-Lloret
Keyword(s):  
Language Learning ◽  
Research Agenda ◽  
Task Complexity ◽  
Language Teaching ◽  
Mobile Apps ◽  
Data Driven ◽  
Process Data ◽  
Face To Face ◽  
Technological Affordances ◽  
Specific Learning

Abstract This paper discusses key concepts in the emerging field of technology-mediated task-based language teaching (TMTBLT) and provides a research agenda for moving this sub-field forward in a theoretically sound and data-driven way. We first define TMTBLT and discuss the importance of considering technological affordances and specific learning contexts when matching individual technologies with particular tasks. We then explore the notion of task, specifically task complexity and sequencing, and how the introduction of technology may interact and modify tasks' features. Next, we examine the use of mobile apps and social media within a task-based language teaching (TBLT) framework and highlight areas primed for exploration or in need of reconciliation. Finally, we call for TMTBLT studies to capture and evaluate learner process data. Within each area above we propose a series of specific research tasks that incrementally build on previous research in both face-to-face and technology-mediated environments, which may help us better understand how tasks and technologies intersect to promote language learning.

scholarly journals Testing the limits of data-driven learning: language proficiency and training

ReCALL ◽  
2009 ◽  
Vol 21 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Alex Boulton
Keyword(s):  
Language Learning ◽  
Language Proficiency ◽  
Empirical Studies ◽  
Data Driven ◽  
First Year ◽  
Single Experiment ◽  
First Year Students ◽  
Essential Condition ◽  
Corpus Data ◽  
Random Groups

AbstractThe potential for corpora in language learning has attracted a significant amount of attention in recent years, including in the form of data-driven learning (DDL). Careful not to appear to over-promote the field, enthusiasts have urged caution in its application, in particular with regard to lower-level learners, and have argued that extensive learner-training in corpus techniques is an essential condition for DDL to be successful. Such limits seem eminently reasonable, but there is a notable dearth of empirical studies to support them. This paper describes a simple experiment to see how lower-level learners cope with corpus data with no prior training.The language focus here is on linking adverbials in English, which are renowned to be difficult to teach using traditional methods. The subjects are 132 first-year students at an engineering college in France of roughly intermediate and lower levels of English. They were divided into random groups to compare their ability to deal with the target items using traditional sources (extracts from a bilingual dictionary or a grammar/usage manual) or corpus data (short contexts or truncated concordances). Performance was tested prior to the experiment, subsequently to check ability to use the different information sources as a reference, and later to test recall.No evidence was found that traditional sources promote better recall, and corpus data seemed to be more effective for reference purposes. While the results of any single experiment must be treated with caution, these findings suggest the need for more empirical studies to complement the theoretical arguments and qualitative data which currently dominate the discussions of DDL.

scholarly journals Corpus-Based Data-Driven Learning to Augment L2 Students’ Vocabulary Repertoire

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Ira Rasikawati
Keyword(s):  
Language Learning ◽  
English For Academic Purposes ◽  
Language Instruction ◽  
Data Driven ◽  
Future Research ◽  
Second Language Instruction ◽  
Research Support ◽  
Inquiry Based Learning ◽  
Research Findings ◽  
English Speaking

Corpus-based data-driven learning (DDL) is an inductive instructional approach using computer-generated concordances. It provides students with the opportunity to analyze different language forms across contexts found in the concordance output. The idea of engaging students to discover the language rules and patterns from authentic learning materials is central to the theory of inquiry-based learning. Despite the robust research support, however, DDL has not been widely adopted, in part because of a dearth of practical and specific recommendations for teachers. More studies are needed to corroborate the claim that the approach can promote the development of different language learning areas effectively. This article synthesizes relevant theories and research findings on the use of DDL for second language instruction and illuminates the understanding of how corpus-based vocabulary instructional strategies may work in English for Academic Purposes (EAP) courses in non-English speaking countries. The study recommendations include a corpus-based DDL framework to expand students’ vocabulary and suggestions for future research. 

Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning

ReCALL ◽  
2019 ◽  
Vol 31 (3) ◽  
pp. 255-275 ◽  
Author(s):  
Peter Crosthwaite ◽  
Lillian L.C. Wong ◽  
Joyce Cheung
Keyword(s):  
Language Learning ◽  
Study Data ◽  
Data Driven ◽  
Postgraduate Students ◽  
Course Materials ◽  
Domain Specific ◽  
Part Of Speech ◽  
Usage Patterns ◽  
And Function ◽  
Thesis Writing

AbstractData-driven learning (DDL; Johns, 1991), involving students’ hands-on use of corpora for self-guided language learning, is a methodology now increasingly used in many tertiary contexts to enhance the teaching of disciplinary postgraduate thesis writing. However, there are still few studies tracking students’ actual engagement with corpora for DDL. This mixed-methods study reports on the tracking of students’ corpus use via a purpose-built corpus query and data visualisation platform integrated into a large postgraduate disciplinary thesis writing program at a university in Hong Kong. Data on corpus usage history (e.g. times of access, duration of use), query syntax (e.g. query lexis/phraseology and use of wildcards and part-of-speech tags), query function (e.g. frequency lists/distribution, concordance sorting and collocation) and query filters (e.g. searches by faculty, discipline, or thesis section) were collected from 327 students spanning over 11,000 individual corpus queries. The results show significant interdisciplinary and inter-/intra-user trends and variation in the use of particular corpus functions and query syntax adopted by corpus users. Students varied in the type of knowledge (e.g. domain-specific, language-specific) they were accessing, and frequently went beyond the exemplars of the DDL course materials to generate unique queries under their own initiative. Qualitative case study data from three corpus users’ activity logs also show distinctive individual corpus engagement by query frequency and function. These data provide a clearer insight into what students actually do during DDL and the different directions and trajectories that individual users take as a result of DDL. All accompanying DDL tasks are also included as supplementary materials.

scholarly journals Developing and implementing an English-Spanish literary parallel audio-textual corpus for data-driven ESL learning

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Michael Lang ◽  
Xavier Gómez Guinovart
Keyword(s):  
Language Learning ◽  
Speech Production ◽  
Language Learners ◽  
Data Driven ◽  
Literary Texts ◽  
Speech Corpus ◽  
Spanish Translation ◽  
Spanish Speaking

ABSTRACT The purpose of this paper is to present the LITTERA corpus, an English-Spanish literary parallel speech corpus created for the purpose of language learning, and to sketch out a few pedagogical applications for the study of English phonology by Spanish-speaking language learners. It is composed of 25 literary texts that have been aligned with the Spanish translation and are accompanied by audio from the corresponding audiobooks. In this article, we will detail its conception, composition and features at length, as well as provide a few examples of how LITTERA can be applied in language learning, particularly within the realm of oral comprehension and speech production.

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