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Whether teaching in a classroom or a Massive Online Open Course it is crucialto present the material in a way that benefits the audience as a whole. Weidentify two important tasks to solve towards this objective, 1 group studentsso that they can maximally benefit from peer interaction and 2 find an optimalschedule of the educational material for each group. Thus, in this paper, wesolve the problem of team formation and content scheduling for education. Givena time frame d, a set of students S with their required need to learn differentactivities T and given k as the number of desired groups, we study the problemof finding k group of students. The goal is to teach students within time framed such that their potential for learning is maximized and find the bestschedule for each group. We show this problem to be NP-hard and develop apolynomial algorithm for it. We show our algorithm to be effective both onsynthetic as well as a real data set. For our experiments, we use real data onstudents' grades in a Computer Science department. As part of our contribution,we release a semi-synthetic dataset that mimics the properties of the realdata.

Team Formation for Scheduling Educational Material in Massive Online Classes