Multi-granulariy Time-based Transformer for Knowledge Tracing
Project Overview
The document explores the transformative role of generative AI in education, focusing on a novel Transformer architecture designed for knowledge tracing, which predicts student performance on standardized tests by leveraging historical data. This approach enhances personalized learning experiences, allows for early interventions, and supports adaptive learning tailored to individual student needs. By employing advanced AI and deep learning models, the proposed system significantly surpasses traditional methods in both accuracy and efficiency, showcasing its potential as a scalable solution within educational environments. The findings highlight the importance of integrating AI technologies to optimize student outcomes and facilitate a more responsive and effective learning ecosystem. Overall, the document underscores the promise of generative AI in revolutionizing educational practices and improving student engagement and success rates.
Key Applications
Transformer architecture for predicting student performance
Context: Educational assessments, targeting students preparing for standardized tests like the SAT and TOEIC.
Implementation: Utilizes historical student data and multi-granularity temporal features to predict outcomes.
Outcomes: Achieved state-of-the-art performance in predicting student outcomes with fewer feature engineering efforts.
Challenges: Capturing the dynamic and complex nature of student behavior over time.
Implementation Barriers
Technical Barrier
Difficulty in capturing the dynamic and complex nature of student behavior that can vary widely over time.
Proposed Solutions: Leveraging deep learning models and multi-granularity features to improve prediction accuracy.
Project Team
Tong Zhou
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Tong Zhou
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