Search Results for author:
Found 3 papers, 0 papers with code
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Traditional learning-based approaches to student modeling (e. g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability.
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning
To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e. g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage.
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
Our methodology for predicting in-video quiz performance is based on three key ideas we develop.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
