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Recomposing Small Learning Groups at Scale—A Data-driven Approach and a Simulation Experiment

dc.contributor.authorZheng, Zhilin
dc.contributor.authorPinkwart, Niels
dc.contributor.editorIgel, Christoph
dc.contributor.editorUllrich, Carsten
dc.contributor.editorWessner Martin
dc.date.accessioned2017-10-05T22:32:16Z
dc.date.available2017-10-05T22:32:16Z
dc.date.issued2017
dc.description.abstractGroup re-composition has thus far been rarely studied. The recent emergence of large scale online learning contexts (e.g. MOOCs) might bring about an opportunity for its application due to the reported high drop-out rate. In this paper, we propose a novel data-driven approach to address the problem of group re-composition. Through a simulation experiment, we saw its capability in decreasing the drop-out rate in groups and bringing more cohesive groups when compared against a random grouping strategy.en
dc.identifier.isbn978-3-88579-667-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/4857
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBildungsräume 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-273
dc.subjectGroup Formation
dc.subjectGroup Re-composition
dc.subjectGroup Dynamics
dc.subjectMOOC
dc.subjectLearning Analytics.
dc.titleRecomposing Small Learning Groups at Scale—A Data-driven Approach and a Simulation Experimenten
gi.citation.endPage332
gi.citation.startPage321
gi.conference.date05.-08.09.2017
gi.conference.locationChemnitz

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