@inproceedings{zhou-etal-2019-intelligent,
title = "An Intelligent Testing Strategy for Vocabulary Assessment of {C}hinese Second Language Learners",
author = "Zhou, Wei and
Hu, Renfen and
Sun, Feipeng and
Huang, Ronghuai",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil\'an, Ildik\'o and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4403/",
doi = "10.18653/v1/W19-4403",
pages = "21--29",
abstract = "Vocabulary is one of the most important parts of language competence. Testing of vocabulary knowledge is central to research on reading and language. However, it usually costs a large amount of time and human labor to build an item bank and to test large number of students. In this paper, we propose a novel testing strategy by combining automatic item generation (AIG) and computerized adaptive testing (CAT) in vocabulary assessment for Chinese L2 learners. Firstly, we generate three types of vocabulary questions by modeling both the vocabulary knowledge and learners' writing error data. After evaluation and calibration, we construct a balanced item pool with automatically generated items, and implement a three-parameter computerized adaptive test. We conduct manual item evaluation and online student tests in the experiments. The results show that the combination of AIG and CAT can construct test items efficiently and reduce test cost significantly. Also, the test result of CAT can provide valuable feedback to AIG algorithms."
}
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%0 Conference Proceedings
%T An Intelligent Testing Strategy for Vocabulary Assessment of Chinese Second Language Learners
%A Zhou, Wei
%A Hu, Renfen
%A Sun, Feipeng
%A Huang, Ronghuai
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F zhou-etal-2019-intelligent
%X Vocabulary is one of the most important parts of language competence. Testing of vocabulary knowledge is central to research on reading and language. However, it usually costs a large amount of time and human labor to build an item bank and to test large number of students. In this paper, we propose a novel testing strategy by combining automatic item generation (AIG) and computerized adaptive testing (CAT) in vocabulary assessment for Chinese L2 learners. Firstly, we generate three types of vocabulary questions by modeling both the vocabulary knowledge and learners’ writing error data. After evaluation and calibration, we construct a balanced item pool with automatically generated items, and implement a three-parameter computerized adaptive test. We conduct manual item evaluation and online student tests in the experiments. The results show that the combination of AIG and CAT can construct test items efficiently and reduce test cost significantly. Also, the test result of CAT can provide valuable feedback to AIG algorithms.
%R 10.18653/v1/W19-4403
%U https://aclanthology.org/W19-4403/
%U https://doi.org/10.18653/v1/W19-4403
%P 21-29
Markdown (Informal)
[An Intelligent Testing Strategy for Vocabulary Assessment of Chinese Second Language Learners](https://aclanthology.org/W19-4403/) (Zhou et al., BEA 2019)
ACL