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
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 217–222
- Language:
- URL:
- https://aclanthology.org/W18-0525
- DOI:
- 10.18653/v1/W18-0525
- Cite (ACL):
- Anton Osika, Susanna Nilsson, Andrii Sydorchuk, Faruk Sahin, and Anders Huss. 2018. Second Language Acquisition Modeling: An Ensemble Approach. In Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 217–222, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Second Language Acquisition Modeling: An Ensemble Approach (Osika et al., BEA 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/W18-0525.pdf