  One of the most ambitious use cases of computer-assisted learning is to build
a recommendation system for lifelong learning. Most recommender algorithms
exploit similarities between content and users, overseeing the necessity to
leverage sensible learning trajectories for the learner. Lifelong learning thus
presents unique challenges, requiring scalable and transparent models that can
account for learner knowledge and content novelty simultaneously, while also
retaining accurate learners representations for long periods of time. We
attempt to build a novel educational recommender, that relies on an integrative
approach combining multiple drivers of learners engagement. Our first step
towards this goal is TrueLearn, which models content novelty and background
knowledge of learners and achieves promising performance while retaining a
human interpretable learner model.
