Manipulating the Difficulty of C-Tests

Ji-Ung Lee, Erik Schwan, Christian M. Meyer


Abstract
We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically. This is a crucial step towards generating learner-adaptive exercises for self-directed language learning and preparing language assessment tests. To reach the desired difficulty level, we manipulate the size and the distribution of gaps based on absolute and relative gap difficulty predictions. We evaluate our approach in corpus-based experiments and in a user study with 60 participants. We find that both strategies are able to generate C-tests with the desired difficulty level.
Anthology ID:
P19-1035
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
360–370
Language:
URL:
https://www.aclweb.org/anthology/P19-1035
DOI:
10.18653/v1/P19-1035
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1035.pdf
Supplementary:
 P19-1035.Supplementary.pdf
Software:
 P19-1035.Software.zip