Manipulating the difficulty of C-Tests
| datacite.relation.isCitedBy | https://doi.org/10.18653/v1/P19-1035 | |
| dc.contributor.author | Lee, Ji-Ung | |
| dc.contributor.author | Meyer, Christian M. | |
| dc.contributor.author | Schwan, Erik | |
| dc.date.accessioned | 2021-04-19T07:43:58Z | |
| dc.date.available | 2021-04-19T07:43:58Z | |
| dc.date.created | 2019-07 | |
| dc.date.issued | 2021-04-19 | |
| dc.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. | en_US |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2704 | |
| dc.language.iso | en | en_US |
| dc.rights.license | CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0) | |
| dc.subject.classification | 4.43-06 | |
| dc.subject.ddc | 004 | |
| dc.title | Manipulating the difficulty of C-Tests | en_US |
| dc.type | Dataset | en_US |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| person.identifier.orcid | 0000-0002-8673-7665 | |
| person.identifier.orcid | #PLACEHOLDER_PARENT_METADATA_VALUE# | |
| tuda.history.classification | Version=2020-2024;409-06 Informationssysteme, Prozess- und Wissensmanagement | |
| tuda.project | HA(Hessen Agentur) | 521/17-03 | a! automated languag | |
| tuda.project | DFG | GRK1994 | TPGurevychGRK1994 | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 5 of 5
| Name | Description | Size | Format | |
|---|---|---|---|---|
| data.zip | 13.81 KB | ZIP-Archivdateien | ||
| DifficultyPrediction.jar | Difficulty Prediction model (using all features) | 99.88 MB | Unknown data format | |
| model_inc | SVM model (pickle file) to measure the relative change in difficulty when increasing the gap size. | 261.57 KB | Unknown data format | |
| model_dec | SVM model (pickle file) to measure the relative change in difficulty when decreasing the gap size. | 163.58 KB | Unknown data format | |
| wp_eng_lem_nc_c.zip | Semantic analysis calculated on the Wikipedia corpus | 845.53 MB | ZIP-Archivdateien |