Show simple item record

dc.contributor.authorBahargam, Sanaz
dc.contributor.authorErdos, Dóra
dc.contributor.authorBestavros, Azer
dc.contributor.authorTerzi, Evimaria
dc.date.accessioned2017-11-22T15:08:19Z
dc.date.available2017-11-22T15:08:19Z
dc.date.issued2017
dc.identifierhttp://arxiv.org/abs/1703.08762v1
dc.identifier.citationSanaz Bahargam, Dóra Erdos, Azer Bestavros, Evimaria Terzi. "Team formation for scheduling educational material in massive online classes."
dc.identifier.urihttps://hdl.handle.net/2144/25722
dc.description.abstractWhether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data.en_US
dc.language.isoen_USen_US
dc.publisherarXiv, Cornell University Libraryen_US
dc.subjectTeam formationen_US
dc.subjectClusteringen_US
dc.subjectPartitioningen_US
dc.subjectTeamsen_US
dc.subjectMOOC (Massive online open course)en_US
dc.subjectArtificial intelligenceen_US
dc.titleTeam formation for scheduling educational material in massive online classesen_US
dc.typeArticleen_US
pubs.elements-sourcearxiven_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
dc.identifier.orcid0000-0003-0798-8835 (Bestavros, Azer)


Files in this item

This item appears in the following Collection(s)

Show simple item record