Feature engineering for second language acquisition modeling

Guanliang Chen, Claudia Hauff, Geert-Jan Houben

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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Abstract

Knowledge tracing serves as a keystone in delivering personalized education. However, few works attempted to model students' knowledge state in the setting of Second Language Acquisition. The Duolingo Shared Task on Second Language Acquisition Modeling (Settles et al., 2018) provides students' trace data that we extensively analyze and engineer features from for the task of predicting whether a student will correctly solve a vocabulary exercise. Our analyses of students' learning traces reveal that factors like exercise format and engagement impact their exercise performance to a large extent. Overall, we extracted 23 different features as input to a Gradient Tree Boosting framework, which resulted in an AUC score of between 0.80 and 0.82 on the official test set.

Original languageEnglish
Title of host publicationNAACL HLT 2018, Innovative Use of NLP for Building Educational Applications
Subtitle of host publicationProceedings of the Thirteenth Workshop
EditorsJoel Tetreault, Jill Burstein, Ekaterina Kochmar, Claudia Leacock, Helen Yannakoudakis
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Pages356-364
Number of pages9
ISBN (Electronic)9781948087117
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventNorth American Association for Computational Linguistics 2018: Human Language Technologies - Hyatt Regency, New Orleans, United States of America
Duration: 1 Jun 20186 Jun 2018
Conference number: 16th
http://naacl2018.org/
https://www.aclweb.org/anthology/volumes/N18-1/ (Proceedings )

Conference

ConferenceNorth American Association for Computational Linguistics 2018
Abbreviated titleNAACL HLT 2018
Country/TerritoryUnited States of America
CityNew Orleans
Period1/06/186/06/18
Internet address

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