@inproceedings{hou-etal-2019-modeling,
title = "Modeling language learning using specialized Elo rating",
author = "Hou, Jue and
Maximilian, Koppatz and
Hoya Quecedo, Jos{\'e} Mar{\'\i}a and
Stoyanova, Nataliya and
Yangarber, Roman",
editor = "Yannakoudakis, Helen and
Kochmar, Ekaterina and
Leacock, Claudia and
Madnani, Nitin and
Pil{\'a}n, Ildik{\'o} and
Zesch, Torsten",
booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4451",
doi = "10.18653/v1/W19-4451",
pages = "494--506",
abstract = "Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.",
}
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<abstract>Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.</abstract>
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%0 Conference Proceedings
%T Modeling language learning using specialized Elo rating
%A Hou, Jue
%A Maximilian, Koppatz
%A Hoya Quecedo, José María
%A Stoyanova, Nataliya
%A Yangarber, Roman
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F hou-etal-2019-modeling
%X Automatic assessment of the proficiency levels of the learner is a critical part of Intelligent Tutoring Systems. We present methods for assessment in the context of language learning. We use a specialized Elo formula used in conjunction with educational data mining. We simultaneously obtain ratings for the proficiency of the learners and for the difficulty of the linguistic concepts that the learners are trying to master. From the same data we also learn a graph structure representing a domain model capturing the relations among the concepts. This application of Elo provides ratings for learners and concepts which correlate well with subjective proficiency levels of the learners and difficulty levels of the concepts.
%R 10.18653/v1/W19-4451
%U https://aclanthology.org/W19-4451
%U https://doi.org/10.18653/v1/W19-4451
%P 494-506
Markdown (Informal)
[Modeling language learning using specialized Elo rating](https://aclanthology.org/W19-4451) (Hou et al., BEA 2019)
ACL
- Jue Hou, Koppatz Maximilian, José María Hoya Quecedo, Nataliya Stoyanova, and Roman Yangarber. 2019. Modeling language learning using specialized Elo rating. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 494–506, Florence, Italy. Association for Computational Linguistics.