Modeling language learning using specialized Elo ratings

Nataliya Stoyanova

doi:10.18653/v1/W19-4451

Modeling language learning using specialized Elo ratings

Nataliya Stoyanova^*

^*Corresponding author

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
Pages	494-506
Number of pages	13
DOIs	https://doi.org/10.18653/v1/W19-4451
Publication status	Published - 2019
Event	Innovative Use of NLP for Building Educational Applications - Firenze Duration: 2 Aug 2019 → 2 Aug 2019

Workshop

Workshop	Innovative Use of NLP for Building Educational Applications
City	Firenze
Period	2/8/19 → 2/8/19

Keywords

Russian language
computer-aided language learning
e-learning

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10.18653/v1/W19-4451

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title = "Modeling language learning using specialized Elo ratings",

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.",

keywords = "Russian language, computer-aided language learning, e-learning, Russian language, computer-aided language learning, e-learning",

author = "Nataliya Stoyanova",

year = "2019",

doi = "10.18653/v1/W19-4451",

language = "English",

isbn = "9781950737345",

pages = "494--506",

booktitle = "Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications",

note = "Innovative Use of NLP for Building Educational Applications ; Conference date: 02-08-2019 Through 02-08-2019",

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TY - GEN

T1 - Modeling language learning using specialized Elo ratings

AU - Stoyanova, Nataliya

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Russian language

KW - computer-aided language learning

KW - e-learning

KW - Russian language

KW - computer-aided language learning

KW - e-learning

UR - http://hdl.handle.net/10807/229130

U2 - 10.18653/v1/W19-4451

DO - 10.18653/v1/W19-4451

M3 - Conference contribution

SN - 9781950737345

SP - 494

EP - 506

BT - Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

T2 - Innovative Use of NLP for Building Educational Applications

Y2 - 2 August 2019 through 2 August 2019

ER -

Modeling language learning using specialized Elo ratings

Abstract

Workshop

Keywords

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