2017/03/25 by Sanaz Bahargam, Dóra Erdös, Bahargam, Sanaz +5
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Online Learning and Analytics #Recommender Systems and Techniques #Scheduling and Timetabling Solutions
paper · pdf · doi:10.48550/arxiv.1703.08762
openalex publication_date 2017/03/25 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Whether teaching in a classroom or a Massive Online Open Course it is crucial\nto present the material in a way that benefits the audience as a whole. We\nidentify two important tasks to solve towards this objective, 1 group students\nso that they can maximally benefit from peer interaction and 2 find an optimal\nschedule of the educational material for each group. Thus, in this paper, we\nsolve the problem of team formation and content scheduling for education. Given\na time frame d, a set of students S with their required need to learn different\nactivities T and given k as the number of desired groups, we study the problem\nof finding k group of students. The goal is to teach students within time frame\nd such that their potential for learning is maximized and find the best\nschedule for each group. We show this problem to be NP-hard and develop a\npolynomial algorithm for it. We show our algorithm to be effective both on\nsynthetic as well as a real data set. For our experiments, we use real data on\nstudents' grades in a Computer Science department. As part of our contribution,\nwe release a semi-synthetic dataset that mimics the properties of the real\ndata.\n