  Spaced repetition is among the most studied learning strategies in the
cognitive science literature. It consists in temporally distributing exposure
to an information so as to improve long-term memorization. Providing students
with an adaptive and personalized distributed practice schedule would benefit
more than just a generic scheduler. However, the applicability of such adaptive
schedulers seems to be limited to pure memorization, e.g. flashcards or foreign
language learning. In this article, we first frame the research problem of
optimizing an adaptive and personalized spaced repetition scheduler when
memorization concerns the application of underlying multiple skills. To this
end, we choose to rely on a student model for inferring knowledge state and
memory dynamics on any skill or combination of skills. We argue that no
knowledge tracing model takes both memory decay and multiple skill tagging into
account for predicting student performance. As a consequence, we propose a new
student learning and forgetting model suited to our research problem: DAS3H
builds on the additive factor models and includes a representation of the
temporal distribution of past practice on the skills involved by an item. In
particular, DAS3H allows the learning and forgetting curves to differ from one
skill to another. Finally, we provide empirical evidence on three real-world
educational datasets that DAS3H outperforms other state-of-the-art EDM models.
These results suggest that incorporating both item-skill relationships and
forgetting effect improves over student models that consider one or the other.
