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Open Access

Manipulating the difficulty of C-Tests

Thumbnail Image

Files

data.zip (13.81 KB)
 DifficultyPrediction.jar (Difficulty Prediction model (using all features)) (99.88 MB)
 model_inc (SVM model (pickle file) to measure the relative change in difficulty when increasing the gap size.) (261.57 KB)
 model_dec (SVM model (pickle file) to measure the relative change in difficulty when decreasing the gap size.) (163.58 KB)
 wp_eng_lem_nc_c.zip (Semantic analysis calculated on the Wikipedia corpus) (845.53 MB)

Date

2021-04-19

Type

Dataset

Authors

Lee, Ji-Ung
Meyer, Christian M.ORCID 0000-0002-8673-7665
Schwan, Erik

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

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.

Keywords

Citation

Identifier

https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2704

Endorsement

Related Resources

Is Cited By
https://doi.org/10.18653/v1/P19-1035

DFG Classification

4.43-06 Datenmanagement, datenintensive Systeme, Informatik-Methoden in der Wirtschaftsinformatik

Project(s)

HA(Hessen Agentur) | 521/17-03 | a! automated languag
DFG | GRK1994 | TPGurevychGRK1994

Faculty

Collections

C-Tests

License

CC BY 4.0 - Attribution 4.0 International
Except where otherwise noted, this license is described as CC BY 4.0 - Attribution 4.0 International