This paper wants to explore the possibilities and chances of DDL for Language Teaching and Learning. In doing, the focus is on, firstly, in which way DDL and immanently the use of corpora differ from tradition approaches in language teaching and learning. The introduction of corpora has shown that the relationship between grammar and lexis is more complex than previously thought. Corpora have led to a lexico-grammatical approach to language, informing language teaching and syllabus design, whereas DDL allows learners and teachers to work with frequent patterns of language in context. Secondly, this paper will provide an overview of the various possibilities of employing DDL in the classroom. DDL language teaching and learning activities involve using corpora and concordancers to actively engage learners in discovering and defining patterns of language use. Lastly, the practical implications of DDL and therefore the limitations of DDL will be considered having a closer look into the pedagogical underpinnings and underlying philosophy of DDL. DDL is a language teaching approach that aims to give learners direct access to authentic language data without the mediation of textbooks or teachers. However, as will be argued, this approach may not be suitable for novice learners due to cognitive limitations, overwhelming amounts of data, and lack of guidance.
Table of Contents
1. Introduction
2. Corpora and Language Teaching
3. DDL Language Teaching and Learning Activities
3.1 Types of activities
3.2 Corpora tools
3.3 Purpose-built corpora
4. Pedagogical Perspective on DDL
5. Conclusion
Research Objectives and Core Topics
This paper explores the potential of Data-Driven Learning (DDL) in language pedagogy, investigating how the shift from traditional rule-based teaching to corpus-based approaches influences language acquisition. It specifically examines the practical classroom applications, technological requirements, and the pedagogical limitations of implementing DDL for learners of varying proficiency levels.
- The transition from traditional lexis-grammar separation to a lexico-grammatical approach
- Methodological classification of DDL activities (pattern hunting vs. pattern defining)
- Technical infrastructure for DDL (concordancers, POS tagging, and collocates tools)
- The role and classification of purpose-built corpora (learner vs. parallel corpora)
- Pedagogical challenges: cognitive load, learner motivation, and the need for guidance
Excerpt from the Book
3.2 Corpora tools
Moreover, these activities are only possible with access to a corpus and a corresponding concordancer, both of which are important since they set the formal framework for DDL. A concordancer is a software tool that allows learners and teachers to search for words and phrases in a corpus and see them in context. Concordancers display the search results as concordance lines, which show the searched word or phrase in the context of its occurrence in the text. Concordance lines are mainly displayed as key word in context (KWIC), where searched-for word or phrase is shown in the center of the line, with all the words from the left and right context aligned vertically. KWIC displays make it easy to see patterns in the way words are used and to identify collocations. This format is helpful for learners to quickly scan the search results and see how the keyword is used in different contexts (Boulton & Cobb 2017).
Another important feature of concordancers is the part of speech (POS) tagging. POS tagging is used to identify the syntactic category of each word in a corpus. POS tags can be used to search for specific types of words, such as nouns or verbs, and to analyze patterns of language use. By identifying the part of speech of each word in a corpus, learners can better understand the syntactic structure of texts and the functions of different types of words in context. For example, a learner who needs to use adjectives in a specific position can use a corpus to search for examples of adjectives used in that position in order to gain a better understanding of the pattern (Boulton 2017).
Summary of Chapters
1. Introduction: Presents the necessity of effective language exposure and introduces Data-Driven Learning (DDL) as a corpus-based pedagogical approach.
2. Corpora and Language Teaching: Discusses the paradigm shift from traditional rule-based instruction to a lexico-grammatical perspective facilitated by corpora.
3. DDL Language Teaching and Learning Activities: Outlines specific pedagogical activities, the essential tools required, and the utility of specialized corpora for language instruction.
4. Pedagogical Perspective on DDL: Critically evaluates the constructivist roots of DDL and the necessity of providing guidance to learners to mitigate cognitive overload.
5. Conclusion: Summarizes the potential of DDL to revolutionize language learning while emphasizing the need for further research focused on novice learner requirements.
Keywords
Data-Driven Learning, DDL, language corpora, language teaching, concordancing, KWIC, lexico-grammatical, pattern hunting, pattern defining, learner autonomy, cognitive load theory, constructivism, language acquisition, corpus linguistics, pedagogical methodology.
Frequently Asked Questions
What is the core focus of this research paper?
This paper examines the application of Data-Driven Learning (DDL) in language education, evaluating how it utilizes corpus data to facilitate language instruction and discovery compared to traditional methods.
What are the primary themes discussed in the work?
The central themes include the shift toward a lexico-grammatical understanding of language, the practical use of concordancers, the differentiation between pattern hunting and defining, and the pedagogical considerations regarding learner proficiency level and guidance.
What is the ultimate objective of the study?
The objective is to explore the chances and possibilities of DDL for language teaching and learning, while critically identifying limitations and the necessity for more structured implementation strategies for novice learners.
Which methodology is described for classroom application?
The paper describes the use of DDL activities involving concordancers to actively engage learners in discovering patterns of language, specifically through pattern hunting (preparation) and pattern defining (contextual model searching).
What does the main body of the text cover?
The main body covers the transition from rule-based to corpus-based teaching, the functional use of digital corpora tools (such as POS tagging and collocates tools), categorization of purpose-built corpora, and a critical look at the constructivist philosophy behind DDL.
Which keywords best characterize this work?
Key terms include Data-Driven Learning, language corpora, pedagogy, concordancing, constructivism, lexico-grammatical approaches, and learner autonomy.
Why might DDL be challenging for novice language learners?
According to the paper, novices may struggle due to cognitive limitations, the overwhelming volume of authentic data, and the difficulty of interpreting raw concordance lines without proper pedagogical guidance.
How does the Cognitive Load Theory relate to the efficacy of DDL?
The paper applies the Cognitive Load Theory to argue that an overly autonomous, constructivist approach can overwhelm a learner's working memory, which in turn leads to poorer learning outcomes compared to a guided approach.
What role do "purpose-built" corpora play in the proposed model?
Purpose-built corpora, such as learner or parallel corpora, offer specific advantages by allowing for Contrastive Interlanguage Analysis (CIA), helping learners identify error patterns and realize idiomatic differences between their native language and the target language.
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- Melih Kemerli (Autor:in), 2023, Application of Data Driven Learning (DDL) for Language Teaching and Learning, München, GRIN Verlag, https://www.grin.com/document/1361840
