Improving Low-Resource Knowledge Tracing Tasks with Pre-training and Fine-tuning

Core Concepts

The author proposes LoReKT, a framework that leverages pre-training on rich-resource datasets and fine-tuning on low-resource datasets to enhance knowledge tracing performance.

Abstract

The paper introduces LoReKT, a framework for improving knowledge tracing in low-resource scenarios. It utilizes pre-training on rich datasets and fine-tuning strategies. The model achieves superior results in AUC and Accuracy compared to baselines across various datasets.
Knowledge tracing is crucial in Intelligent Tutoring Systems to predict student performance based on past interactions. Deep learning models like DKT have shown promise but face challenges with limited data. LoReKT addresses this by transferring knowledge from rich to low-resource datasets through pre-training and fine-tuning.
The importance mechanism in LoReKT prioritizes updating crucial parameters during fine-tuning, preventing overfitting. The model's effectiveness is demonstrated through experiments on public KT datasets, showcasing significant improvements in AUC and Accuracy.
By incorporating data type embeddings and dataset embeddings, LoReKT enhances the model's ability to integrate information from questions and concepts effectively. This approach leads to improved performance across different KT datasets.

Stats

AS2009: 4,217 students
NIPS34: 1,382,727 interactions
AL2005: 3,679,199 interactions

Quotes

"To ensure our approach can be fairly comparable with other DLKT models, we follow a standardized KT task evaluation protocol."
"Our proposed LoReKT framework demonstrates robust zero-shot capabilities across different disciplines."

Key Insights Distilled From

Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning

by Hengyuan Zha... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06725.pdf

Deeper Inquiries

How does the LoReKT framework address the challenge of overfitting in low-resource KT datasets

The LoReKT framework addresses the challenge of overfitting in low-resource KT datasets by implementing a two-stage approach: pre-training and fine-tuning. In the pre-training stage, the model learns transferable parameters and representations from rich-resource KT datasets. This helps mitigate overfitting by providing a solid foundation for knowledge tracing without directly training on the limited data of low-resource datasets. By leveraging diverse sources of data during pre-training, LoReKT can capture general patterns that are applicable across different datasets, reducing the risk of overfitting to specific dataset nuances.

What are the implications of using importance vectors for fine-tuning deep learning models

Importance vectors play a crucial role in fine-tuning deep learning models as they prioritize updating important parameters while constraining less important ones. By computing importance vectors for each layer based on their impact on the loss function during training on low-resource KT datasets, LoReKT ensures that only significant parameters are updated during fine-tuning. This strategy helps prevent memorization of noisy information and focuses on enhancing model performance where it matters most, leading to improved generalization capabilities and better adaptation to new datasets.

How might the findings of this study impact the development of personalized learning platforms

The findings of this study could have significant implications for the development of personalized learning platforms. By demonstrating the effectiveness of pre-training with transferable knowledge tracing capabilities from rich-resource datasets to low-resource scenarios, LoReKT offers a promising approach to enhance student performance estimation even with limited interaction data available. The use of importance mechanisms in fine-tuning further refines model updates based on parameter significance, potentially leading to more accurate predictions tailored to individual students' needs. Implementing such techniques could improve adaptive learning systems by providing more precise recommendations and personalized educational experiences based on students' unique knowledge mastery levels and learning progressions.