Second Language Acquisition Modeling: An Ensemble Approach

Anton Osika, Susanna Nilsson, Andrii Sydorchuk, Faruk Sahin, Anders Huss


Abstract
Accurate prediction of students’ knowledge is a fundamental building block of personalized learning systems. Here, we propose an 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 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.
Anthology ID:
W18-0525
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
217–222
Language:
URL:
https://www.aclweb.org/anthology/W18-0525
DOI:
10.18653/v1/W18-0525
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0525.pdf