  Predicting the strategy (sequence of concepts) that a student is likely to
use in problem-solving helps Adaptive Instructional Systems (AISs) better adapt
themselves to different types of learners based on their learning abilities.
This can lead to a more dynamic, engaging, and personalized experience for
students. To scale up training a prediction model (such as LSTMs) over
large-scale education datasets, we develop a non-parametric approach to cluster
symmetric instances in the data. Specifically, we learn a representation based
on Node2Vec that encodes symmetries over mastery or skill level since, to solve
a problem, it is natural that a student's strategy is likely to involve
concepts in which they have gained mastery. Using this representation, we use
DP-Means to group symmetric instances through a coarse-to-fine refinement of
the clusters. We apply our model to learn strategies for Math learning from
large-scale datasets from MATHia, a leading AIS for middle-school math
learning. Our results illustrate that our approach can consistently achieve
high accuracy using a small sample that is representative of the full dataset.
Further, we show that this approach helps us learn strategies with high
accuracy for students at different skill levels, i.e., leveraging symmetries
improves fairness in the prediction model.
