  Traditional learning-based approaches to student modeling generalize poorly
to underrepresented student groups due to biases in data availability. In this
paper, we propose a methodology for predicting student performance from their
online learning activities that optimizes inference accuracy over different
demographic groups such as race and gender. Building upon recent foundations in
federated learning, in our approach, personalized models for individual student
subgroups are derived from a global model aggregated across all student models
via meta-gradient updates that account for subgroup heterogeneity. 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. Through experiments on three real-world datasets
from online courses, we demonstrate that our approach obtains substantial
improvements over existing student modeling baselines in predicting student
learning outcomes for all subgroups. Visual analysis of the resulting student
embeddings confirm that our personalization methodology indeed identifies
different activity patterns within different subgroups, consistent with its
stronger inference ability compared with the baselines.
