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Second Language Acquisition Modeling: An Ensemble Approach

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arxiv 1806.04525 v1 pith:DCC7GUU6 submitted 2018-06-09 cs.CL

Anton Osika , Susanna Nilsson , Andrii Sydorchuk , Faruk Sahin , Anders Huss This is my paper · ORCID
classification cs.CL
keywords acquisitionapproachensembleknowledgelanguagemodelmodelingpersonalized
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Accurate prediction of students knowledge is a fundamental building block of personalized learning systems. Here, we propose a novel ensemble model to predict student knowledge gaps. Applying our approach to student trace data from the online educational platform Duolingo we achieved highest score on both evaluation metrics for all three datasets in the 2018 Shared Task on Second Language Acquisition Modeling. We describe our model and discuss relevance of the task compared to how it would be setup in a production environment for personalized education.

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Cited by 1 Pith paper

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  1. Multi-task Learning for Low-resource Second Language Acquisition Modeling

    cs.CL 2019-08 conditional novelty 5.0 of 10

    Multi-task learning across three Duolingo language datasets improves word-level answer prediction in low-resource settings, roughly matching 10x larger single-task training sets.

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