Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails
Project Overview
The document explores the application of generative AI in education through the development of the Explainable Student performance prediction method with Personalized Attention (ESPA), which aims to improve the prediction of student outcomes. It highlights the importance of explainability in these predictions, allowing educators to implement timely interventions. By utilizing a Bidirectional Long Short-Term Memory (BiLSTM) architecture along with attention mechanisms, ESPA effectively analyzes student data to generate interpretable predictions. The findings demonstrate that this approach surpasses existing prediction models, providing deeper insights into the factors that influence student performance. Overall, the use of generative AI in this context not only enhances predictive accuracy but also supports educators in fostering better educational outcomes through targeted assistance.
Key Applications
Explainable Student performance prediction method with Personalized Attention (ESPA)
Context: Higher education, targeting educators and institutions seeking to predict student performance and identify at-risk students.
Implementation: Utilizes relationships in student profiles and course knowledge graphs, employing a BiLSTM architecture and attention mechanisms to predict student outcomes.
Outcomes: Outperforms state-of-the-art models in predicting student performance while providing explainable insights into predictions.
Challenges: Existing methods often lack explainability, making it difficult for educators to trust predictions and intervene appropriately.
Implementation Barriers
Technical Barrier
The complexity of processing and analyzing large volumes of heterogeneous educational data.
Proposed Solutions: Utilization of advanced deep learning architectures (e.g., BiLSTM) and attention mechanisms to effectively process data.
Explainability Barrier
The lack of transparency in traditional prediction models, which are often seen as 'black boxes'.
Proposed Solutions: Incorporating explainable AI techniques within the model to clarify how predictions are made, enhancing educator trust.
Project Team
Kun Niu
Researcher
Xipeng Cao
Researcher
Yicong Yu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Kun Niu, Xipeng Cao, Yicong Yu
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