Second Language Acquisition Modeling: An Ensemble Approach

TL;DR

This paper introduces an ensemble model for predicting student knowledge gaps in second language learning, achieving top performance on shared task datasets and discussing its application in personalized education systems.

Contribution

The paper presents a novel ensemble approach for second language acquisition modeling and demonstrates its effectiveness on real-world educational data.

Findings

Achieved highest scores on all datasets in the 2018 Shared Task.

Demonstrated the model's effectiveness in predicting student knowledge gaps.

Discussed practical considerations for deploying the model in educational settings.

Abstract

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.