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Autor*innen: Beinborn, Lisa; Zesch, Torsten; Gurevych, Iryna
Titel: Candidate evaluation strategies for improved difficulty prediction of language tests
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 , S. 1-11
URL: https://www.ukp.tu-darmstadt.de/fileadmin/user_upload/Group_UKP/CandidateEvaluationStrategies_BEACameraReady.pdf
Dokumenttyp: 4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
Sprache: Englisch
Schlagwörter: 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-Abteilung: Informationszentrum Bildung