Modeling language learning using specialized Elo rating

Jue Hou, Koppatz Maximilian, José María Hoya Quecedo, Nataliya Stoyanova, Roman Yangarber


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.
Anthology ID:
W19-4451
Volume:
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | BEA | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
494–506
Language:
URL:
https://aclanthology.org/W19-4451
DOI:
10.18653/v1/W19-4451
Bibkey:
Cite (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.
Cite (Informal):
Modeling language learning using specialized Elo rating (Hou et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-4451.pdf