Language Learning Tasks and Automatic Analysis of Learner Language: Connecting FLTL and NLP design of ICALL materials supporting use in real-life instruction

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URI: http://hdl.handle.net/10900/67434
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-674344
http://dx.doi.org/10.15496/publikation-8854
Dokumentart: Dissertation
Date: 2015-12
Language: English
Faculty: 5 Philosophische Fakultät
5 Philosophische Fakultät
Department: Allgemeine u. vergleichende Sprachwissenschaft
Advisor: Meurers, Walt Detmar (Prof. Dr.)
Day of Oral Examination: 2012-12-20
DDC Classifikation: 400 - Language and Linguistics
Keywords: Computerlinguistik , Computerunterstütztes Lernen , Fremdsprachenlernen , Spracherwerb
Other Keywords:
intelligent computer-assisted language learning
natural language processing
task-based language teaching
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Abstract:

This thesis studies the application of Natural Language Processing to Foreign Language Teaching and Learning, within the research area of Intelligent Computer- Assisted Language Learning (ICALL). In particular, we investigate the design, the implementation, and the use of ICALL materials to provide learners of foreign languages, particularly English, with automated feedback. We argue that the successful integration of ICALL materials demands a design process considering both pedagogical and computational requirements as equally important. Our investigation pursues two goals. The first one is to integrate into task design insights from Second Language Acquisition and Foreign Language Teaching and Learning with insights from computational linguistic modelling. The second goal is to facilitate the integration of ICALL materials in real-world instruction settings, as opposed to research or lab-oriented instruction settings, by empowering teachers with the methodology and the technology to autonomously author such materials. To achieve the first goal, we propose an ICALL material design process that combines basic principles of Task-Based Language Instruction and Task-Based Test Design with the specification requirements of Natural Language Processing. The relation between pedagogical and computational requirements is elucidated by exploring (i) the formal features of foreign language learning activities, (ii) the complexity and variability of learner language, and (iii) the feasibility of applying computational techniques for the automatic analysis and evaluation of learner responses. To achieve the second goal, we propose an automatic feedback generation strategy that enables teachers to customise the computational resources required to automatically correct ICALL activities without the need for programming skills. This proposal is instantiated and evaluated in real world-instruction settings involving teachers and learners in secondary education. Our work contributes methodologically and empirically to the ICALL field, with a novel approach to the design of materials that highlights the cross-disciplinary and iterative nature of the task. Our findings reveal the strength of characterising tasks both from the perspective of Foreign Language Teaching and Learning and from the perspective of Computational Linguistics as a means to clarify the nature of learning activities. Such a characterisation allows us to identify ICALL materials which are both pedagogically meaningful and computationally feasible. Our results show that teachers can characterise, author and employ ICALL mate- rials as part of their instruction programme, and that the underlying computational machinery can provide the required automatic processing with sufficient efficiency. The authoring tool and the accompanying methodology become a crucial instrument for ICALL research and practice: Teachers are able to design activities for their students to carry out without relying on an expert in Natural Language Processing. Last but not least, our results show that teachers are value the experience very positively as means to engage in technology integration, but also as a means to better apprehend the nature of their instruction task. Moreover, our results show that learners are motivated by the opportunity of using a technology that enhances their learning experience.

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