# Adaptive Learning Material Recommendation in Online Language Education

arXiv: https://arxiv.org/abs/1905.10893 · HTML: https://mlsanity.com/paper/1905.10893/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2019.

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

- [No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths](https://mlsanity.com/paper/2308.03488/) (2023)
- [A Design of A Simple Yet Effective Exercise Recommendation System in K-12 Online Learning](https://mlsanity.com/paper/2206.12291/) (2022)
- [Dual-State Personalized Knowledge Tracing with Emotional Incorporation](https://mlsanity.com/paper/2405.16799/) (2024)
- [Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing](https://mlsanity.com/paper/2309.01179/) (2023)

## Abstract (arXiv)

Recommending personalized learning materials for online language learning is challenging because we typically lack data about the student's ability and the relative difficulty of learning materials. This makes it hard to recommend appropriate content that matches the student's prior knowledge. In this paper, we propose a refined hierarchical knowledge structure to model vocabulary knowledge, which enables us to automatically organize the authentic and up-to-date learning materials collected from the internet. Based on this knowledge structure, we then introduce a hybrid approach to recommend learning materials that adapts to a student's language level. We evaluate our work with an online Japanese learning tool and the results suggest adding adaptivity into material recommendation significantly increases student engagement.