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.

Modeling language learning using specialized Elo ratings / J. Hou, K. Maximilian, J.M. Hoya Quecedo, N. Stoyanova, R. Yangarber - In: Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications / [a cura di] H. Yannakoudakis, E. Kochmar, C. Leacock, N. Madnani, I. Pilán, T. Zesch. - Prima edizione. - Stroudsburg : The Association for Computational Linguistics, 2019 Aug. - ISBN 9781950737345. - pp. 494-506 (( Intervento presentato al 14. convegno Innovative Use of NLP for Building Educational Applications tenutosi a Firenze nel 2019 [10.18653/v1/W19-4451].

Modeling language learning using specialized Elo ratings

N. Stoyanova
;
2019

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.
Russian language; e-learning; computer-aided language learning
Settore L-LIN/21 - Slavistica
Settore INF/01 - Informatica
ago-2019
The Association for Computational Linguistics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/699274
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