  Currently, college-going students are taking longer to graduate than their
parental generations. Further, in the United States, the six-year graduation
rate has been 59% for decades. Improving the educational quality by training
better-prepared students who can successfully graduate in a timely manner is
critical. Accurately predicting students' grades in future courses has
attracted much attention as it can help identify at-risk students early so that
personalized feedback can be provided to them on time by advisors. Prior
research on students' grade prediction include shallow linear models; however,
students' learning is a highly complex process that involves the accumulation
of knowledge across a sequence of courses that can not be sufficiently modeled
by these linear models. In addition to that, prior approaches focus on
prediction accuracy without considering prediction uncertainty, which is
essential for advising and decision making. In this work, we present two types
of Bayesian deep learning models for grade prediction. The MLP ignores the
temporal dynamics of students' knowledge evolution. Hence, we propose RNN for
students' performance prediction. To evaluate the performance of the proposed
models, we performed extensive experiments on data collected from a large
public university. The experimental results show that the proposed models
achieve better performance than prior state-of-the-art approaches. Besides more
accurate results, Bayesian deep learning models estimate uncertainty associated
with the predictions. We explore how uncertainty estimation can be applied
towards developing a reliable educational early warning system. In addition to
uncertainty, we also develop an approach to explain the prediction results,
which is useful for advisors to provide personalized feedback to students.
