Textual complexity as a predictor of difficulty of listening items in language proficiency tests

Anastassia Loukina, Su-Youn Yoon, Jennifer Sakano, Youhua Wei, Kathy Sheehan


Abstract
In this paper we explore to what extent the difficulty of listening items in an English language proficiency test can be predicted by the textual properties of the prompt. We show that a system based on multiple text complexity features can predict item difficulty for several different item types and for some items achieves higher accuracy than human estimates of item difficulty.
Anthology ID:
C16-1306
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
3245–3253
Language:
URL:
https://www.aclweb.org/anthology/C16-1306
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1306.pdf