  We developed a simulator to quantify the effect of exercise ordering on both
student engagement and retention. Our approach combines the construction of
neural network representations for users and exercises using a dynamic matrix
factorization method. We further created a machine learning models of success
and dropout prediction. As a result, our system is able to predict student
engagement and retention based on a given sequence of exercises selected. This
opens the door to the development of versatile reinforcement learning agents
which can substitute the role of private tutoring in exam preparation.

Computers and Society;Artificial Intelligence;Machine Learning