  Students in online courses generate large amounts of data that can be used to
personalize the learning process and improve quality of education. In this
paper, we present the Latent Skill Embedding (LSE), a probabilistic model of
students and educational content that can be used to recommend personalized
sequences of lessons with the goal of helping students prepare for specific
assessments. Akin to collaborative filtering for recommender systems, the
algorithm does not require students or content to be described by features, but
it learns a representation using access traces. We formulate this problem as a
regularized maximum-likelihood embedding of students, lessons, and assessments
from historical student-content interactions. An empirical evaluation on
large-scale data from Knewton, an adaptive learning technology company, shows
that this approach predicts assessment results competitively with benchmark
models and is able to discriminate between lesson sequences that lead to
mastery and failure.
