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Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Standard

Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring. / Pandarova, Irina; Schmidt, Torben; Hartig, Johannes et al.
in: International Journal of Artificial Intelligence in Education, Jahrgang 29, Nr. 3, 15.08.2019, S. 342-367.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Pandarova, I, Schmidt, T, Hartig, J, Boubekki, A, Jones, RD & Brefeld, U 2019, 'Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring', International Journal of Artificial Intelligence in Education, Jg. 29, Nr. 3, S. 342-367. https://doi.org/10.1007/s40593-019-00180-4

APA

Pandarova, I., Schmidt, T., Hartig, J., Boubekki, A., Jones, R. D., & Brefeld, U. (2019). Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring. International Journal of Artificial Intelligence in Education, 29(3), 342-367. https://doi.org/10.1007/s40593-019-00180-4

Vancouver

Pandarova I, Schmidt T, Hartig J, Boubekki A, Jones RD, Brefeld U. Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring. International Journal of Artificial Intelligence in Education. 2019 Aug 15;29(3):342-367. Epub 2019 Mai 13. doi: 10.1007/s40593-019-00180-4

Bibtex

@article{348f671ee47740708ec089a02c9c14c2,
title = "Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring",
abstract = "Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner{\textquoteright}s evolving language ability. A pilot study is presented which aims to advance the (semi-)automatic difficulty scoring of grammar exercise items to be used in dynamic difficulty adaptation in an intelligent language tutoring system for practicing English tenses. In it, methods from item response theory and machine learning are combined with linguistic item analysis in order to calibrate the difficulty of an initial exercise pool of cued gap-filling items (CGFIs) and isolate CGFI features predictive of item difficulty. Multiple item features at the gap, context and CGFI levels are tested and relevant predictors are identified at all three levels. Our pilot regression models reach encouraging prediction accuracy levels which could, pending additional validation, enable the dynamic selection of newly generated items ranging from moderately easy to moderately difficult. The paper highlights further applications of the proposed methodology in the area of adapting language tutoring, item design and second language acquisition, and sketches out issues for future research.",
keywords = "Didactics of English as a foreign language, adaptivity, Intelligent language tutoring systems, Idem difficulty prediction, Item response theory, Machine Learning, Scon language aquisition",
author = "Irina Pandarova and Torben Schmidt and Johannes Hartig and Ahc{\`e}ne Boubekki and Jones, {Roger Dale} and Ulf Brefeld",
note = "doi.org/10.1007/s40593-019-00180-4",
year = "2019",
month = aug,
day = "15",
doi = "10.1007/s40593-019-00180-4",
language = "English",
volume = "29",
pages = "342--367",
journal = "International Journal of Artificial Intelligence in Education",
issn = "1560-4306",
publisher = "Springer Netherlands",
number = "3",

}

RIS

TY - JOUR

T1 - Predicting the Difficulty of Exercise Items for Dynamic Difficulty Adaptation in Adaptive Language Tutoring

AU - Pandarova, Irina

AU - Schmidt, Torben

AU - Hartig, Johannes

AU - Boubekki, Ahcène

AU - Jones, Roger Dale

AU - Brefeld, Ulf

N1 - doi.org/10.1007/s40593-019-00180-4

PY - 2019/8/15

Y1 - 2019/8/15

N2 - Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner’s evolving language ability. A pilot study is presented which aims to advance the (semi-)automatic difficulty scoring of grammar exercise items to be used in dynamic difficulty adaptation in an intelligent language tutoring system for practicing English tenses. In it, methods from item response theory and machine learning are combined with linguistic item analysis in order to calibrate the difficulty of an initial exercise pool of cued gap-filling items (CGFIs) and isolate CGFI features predictive of item difficulty. Multiple item features at the gap, context and CGFI levels are tested and relevant predictors are identified at all three levels. Our pilot regression models reach encouraging prediction accuracy levels which could, pending additional validation, enable the dynamic selection of newly generated items ranging from moderately easy to moderately difficult. The paper highlights further applications of the proposed methodology in the area of adapting language tutoring, item design and second language acquisition, and sketches out issues for future research.

AB - Advances in computer technology and artificial intelligence create opportunities for developing adaptive language learning technologies which are sensitive to individual learner characteristics. This paper focuses on one form of adaptivity in which the difficulty of learning content is dynamically adjusted to the learner’s evolving language ability. A pilot study is presented which aims to advance the (semi-)automatic difficulty scoring of grammar exercise items to be used in dynamic difficulty adaptation in an intelligent language tutoring system for practicing English tenses. In it, methods from item response theory and machine learning are combined with linguistic item analysis in order to calibrate the difficulty of an initial exercise pool of cued gap-filling items (CGFIs) and isolate CGFI features predictive of item difficulty. Multiple item features at the gap, context and CGFI levels are tested and relevant predictors are identified at all three levels. Our pilot regression models reach encouraging prediction accuracy levels which could, pending additional validation, enable the dynamic selection of newly generated items ranging from moderately easy to moderately difficult. The paper highlights further applications of the proposed methodology in the area of adapting language tutoring, item design and second language acquisition, and sketches out issues for future research.

KW - Didactics of English as a foreign language

KW - adaptivity

KW - Intelligent language tutoring systems

KW - Idem difficulty prediction

KW - Item response theory

KW - Machine Learning

KW - Scon language aquisition

UR - http://www.scopus.com/inward/record.url?scp=85065811581&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/272a803a-842d-38d7-b186-cb3092c08c57/

U2 - 10.1007/s40593-019-00180-4

DO - 10.1007/s40593-019-00180-4

M3 - Journal articles

VL - 29

SP - 342

EP - 367

JO - International Journal of Artificial Intelligence in Education

JF - International Journal of Artificial Intelligence in Education

SN - 1560-4306

IS - 3

ER -

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