# Second Language Acquisition Modeling: An Ensemble Approach

arXiv: https://arxiv.org/abs/1806.04525 · HTML: https://mlsanity.com/paper/1806.04525/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Adaptive and Personalized Exercise Generation for Online Language Learning](https://mlsanity.com/paper/2306.02457/) (2023)
- [Text mining in education](https://mlsanity.com/paper/2403.00769/) (2024)
- [T"ubingen-CL at SemEval-2024 Task 1:Ensemble Learning for Semantic Relatedness Estimation](https://mlsanity.com/paper/2410.10585/) (2024)
- [Fair Knowledge Tracing in Second Language Acquisition](https://mlsanity.com/paper/2412.18048/) (2024)

## Abstract (arXiv)

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.