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.
Cite
@article{arxiv.1906.06905,
title = {Manipulating the Difficulty of C-Tests},
author = {Ji-Ung Lee and Erik Schwan and Christian M. Meyer},
journal= {arXiv preprint arXiv:1906.06905},
year = {2019}
}
Comments
To appear as a long paper in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Download our code and data from the user study at github: https://github.com/UKPLab/acl2019-ctest-difficulty-manipulation