Exercise Hierarchical Feature Enhanced Knowledge Tracing
Project Overview
The document explores the application of generative AI in education, focusing on a novel framework for knowledge tracing within intelligent education systems. This framework enhances traditional methods by integrating hierarchical features derived from exercise text, such as knowledge distribution, semantic characteristics, and the difficulty level of exercises. By leveraging these features, the framework aims to improve the accuracy of predicting student performance. Experimental results indicate that this innovative approach significantly outperforms existing models, underscoring the critical role of hierarchical features in achieving more precise knowledge tracing. Overall, the findings suggest that incorporating generative AI into educational systems can lead to enhanced learning outcomes by providing tailored insights into student understanding and performance.
Key Applications
Exercise Hierarchical Feature Enhanced Knowledge Tracing (EHFKT)
Context: Online education systems, targeting students who interact with educational exercises.
Implementation: The framework uses embedding from the Bert model to extract features and employs RNNs to model student responses. It integrates knowledge distribution, semantic features, and difficulty of exercises.
Outcomes: Reported performance improvements in knowledge tracing accuracy, better representation of questions, and precise predictions of student performance.
Challenges: Challenges include the unpredictability of exercise difficulty and the complexity of interpreting semantic clusters.
Implementation Barriers
Technical
Existing knowledge tracing models have limitations in considering various attributes of exercises.
Proposed Solutions: The proposed EHFKT framework addresses these limitations by incorporating hierarchical features and sophisticated modeling techniques.
Data Availability
Lack of open datasets with exercise records that include text information.
Proposed Solutions: The authors created a custom experimental dataset from a real-world online education system.
Project Team
Hanshuang Tong
Researcher
Yun Zhou
Researcher
Zhen Wang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hanshuang Tong, Yun Zhou, Zhen Wang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai