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Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases.
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.
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