Feature engineering for second language acquisition modeling

Guanliang Chen, Claudia Hauff, Geert-Jan Houben

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

1 Citation (Scopus)


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)
Number of pages9
ISBN (Electronic)9781948087117
Publication statusPublished - 2018
Externally publishedYes
EventInnovative Use of NLP for Building Educational Applications 2018 - New Orleans, United States of America
Duration: 5 Jun 20155 Jun 2015
Conference number: 13th


ConferenceInnovative Use of NLP for Building Educational Applications 2018
Country/TerritoryUnited States of America
CityNew Orleans
Internet address

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