Gap-fill Tests for Language Learners: Corpus-Driven Item Generation

Simon Smith; P.V.S. Avinesh; Adam Kilgarriff

Gap-fill Tests for Language Learners: Corpus-Driven Item Generation

Simon Smith, P.V.S. Avinesh, Adam Kilgarriff

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Abstract

Gap-fill exercises have an important role in language teaching. They allow students to demonstrate that they understand vocabulary in context, discouraging memorisation of translations. It is time consuming and difficult for item writers to create good test items, and even then test items are open to Sinclair’s critique of invented examples. We present a system,TEDDCLOG, which automatically generates draft test items from a corpus. TEDDCLOG takes the key (the word which will form the correct answer to the exercise) as input. It finds distractors (the alternative, wrong answers for the multiplechoice question) from a distributional thesaurus, and identifies a collocate of the key that does not occur with the distractors. Next it finds a simple corpus sentence containing
the key and collocate. The system then presents the sentences and distractors
to the user for approval, modification or rejection. The system is implemented using the API to the Sketch Engine, a leading corpus query system. We compare TEDDCLOG with other gap-fill-generation systems, and offer a partial evaluation of the results.
Key Words: gap-fill, Sketch Engine, corpus linguistics, ELT, GDEX, proficiency
testing

Original language	English
Number of pages	7
Publication status	Published - 2010

Bibliographical note

The attached paper is also available online on the International Institute of Information Technology, Hyderabad (India) website at: http://web.iiit.ac.in/~avinesh/papers/TeddclogICON2010.pdf. The paper was given at the ICON-2010: 8th International Conference on Natural Language Processing, Kharagpur, India. The full proceedings have been published by Macmillan Publishers, India - http://www.macmillanindia.com.

Keywords

gap-fill
Sketch Engine
corpus
linguistics
ELT
GDEX
proficiency
testing

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Cite this

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title = "Gap-fill Tests for Language Learners: Corpus-Driven Item Generation",

abstract = "Gap-fill exercises have an important role in language teaching. They allow students to demonstrate that they understand vocabulary in context, discouraging memorisation of translations. It is time consuming and difficult for item writers to create good test items, and even then test items are open to Sinclair{\textquoteright}s critique of invented examples. We present a system,TEDDCLOG, which automatically generates draft test items from a corpus. TEDDCLOG takes the key (the word which will form the correct answer to the exercise) as input. It finds distractors (the alternative, wrong answers for the multiplechoice question) from a distributional thesaurus, and identifies a collocate of the key that does not occur with the distractors. Next it finds a simple corpus sentence containingthe key and collocate. The system then presents the sentences and distractorsto the user for approval, modification or rejection. The system is implemented using the API to the Sketch Engine, a leading corpus query system. We compare TEDDCLOG with other gap-fill-generation systems, and offer a partial evaluation of the results.Key Words: gap-fill, Sketch Engine, corpus linguistics, ELT, GDEX, proficiencytesting",

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N1 - The attached paper is also available online on the International Institute of Information Technology, Hyderabad (India) website at: http://web.iiit.ac.in/~avinesh/papers/TeddclogICON2010.pdf. The paper was given at the ICON-2010: 8th International Conference on Natural Language Processing, Kharagpur, India. The full proceedings have been published by Macmillan Publishers, India - http://www.macmillanindia.com.

PY - 2010

Y1 - 2010

N2 - Gap-fill exercises have an important role in language teaching. They allow students to demonstrate that they understand vocabulary in context, discouraging memorisation of translations. It is time consuming and difficult for item writers to create good test items, and even then test items are open to Sinclair’s critique of invented examples. We present a system,TEDDCLOG, which automatically generates draft test items from a corpus. TEDDCLOG takes the key (the word which will form the correct answer to the exercise) as input. It finds distractors (the alternative, wrong answers for the multiplechoice question) from a distributional thesaurus, and identifies a collocate of the key that does not occur with the distractors. Next it finds a simple corpus sentence containingthe key and collocate. The system then presents the sentences and distractorsto the user for approval, modification or rejection. The system is implemented using the API to the Sketch Engine, a leading corpus query system. We compare TEDDCLOG with other gap-fill-generation systems, and offer a partial evaluation of the results.Key Words: gap-fill, Sketch Engine, corpus linguistics, ELT, GDEX, proficiencytesting

AB - Gap-fill exercises have an important role in language teaching. They allow students to demonstrate that they understand vocabulary in context, discouraging memorisation of translations. It is time consuming and difficult for item writers to create good test items, and even then test items are open to Sinclair’s critique of invented examples. We present a system,TEDDCLOG, which automatically generates draft test items from a corpus. TEDDCLOG takes the key (the word which will form the correct answer to the exercise) as input. It finds distractors (the alternative, wrong answers for the multiplechoice question) from a distributional thesaurus, and identifies a collocate of the key that does not occur with the distractors. Next it finds a simple corpus sentence containingthe key and collocate. The system then presents the sentences and distractorsto the user for approval, modification or rejection. The system is implemented using the API to the Sketch Engine, a leading corpus query system. We compare TEDDCLOG with other gap-fill-generation systems, and offer a partial evaluation of the results.Key Words: gap-fill, Sketch Engine, corpus linguistics, ELT, GDEX, proficiencytesting

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M3 - Paper

ER -

Gap-fill Tests for Language Learners: Corpus-Driven Item Generation

Abstract

Bibliographical note

Keywords

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Cite this