Second Language Acquisition Modeling: An Ensemble Approach
This work addresses personalized learning systems for language education, though it appears incremental as it builds on existing ensemble methods.
The authors tackled the problem of predicting student knowledge gaps in second language acquisition by proposing a novel ensemble model, which achieved the highest scores on all evaluation metrics across three datasets in the 2018 Shared Task on Duolingo data.
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.