Computational Education using Latent Structured Prediction

Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students' current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by learning parameters of a log-linear distribution. For interpretability we propose to constrain the parameter space a-priori by leveraging domain knowledge. We show the benefits of general graphical models and of regularizing the parameter space by evaluation of our models on data collected from a computational education software for children having difficulties in learning mathematics.