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id: 35510

Candidate evaluation strategies for improved difficulty prediction of language tests Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna Book Chapter | Aus: Association for Computational Linguistics (Hrsg.): Proceedings of the 10th Workshop on innovative use of NLP for Building Educational Applications held in conjunction with NAACL 2015 | Denver; CO: Association for Computational Linguistics | 2015 35510 Endnote: Author(s): Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna
Title: Candidate evaluation strategies for improved difficulty prediction of language tests
In: Association for Computational Linguistics (Hrsg.): Proceedings of the 10th Workshop on innovative use of NLP for Building Educational Applications held in conjunction with NAACL 2015, Denver; CO: Association for Computational Linguistics, 2015 , S. 1-11
URL: https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/CandidateEvaluationStrategies_BEACameraReady.pdf
Publication Type: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Language: Englisch
Keywords: Fortschritt; Lernen; Lernerfolg; Prognose; Ranking; Schwierigkeit; Sprachtest; Strategie
Abstract: Language proficiency tests are a useful tool for evaluating learner progress, if the test difficulty fits the level of the learner. In this work, we describe a generalized framework for test difficulty prediction that is applicable to several languages and test types. In addition, we develop two ranking strategies for candidate evaluation inspired by automatic solving methods based on language model probability and semantic relatedness. These ranking strategies lead to significant improvements for the difficulty prediction of cloze tests. (DIPF/Orig.)
DIPF-Departments: Informationszentrum Bildung