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dc.contributor.authorKonert, Johannesde_DE
dc.contributor.authorBurlak, Dmitrijde_DE
dc.contributor.authorSteinmetz, Ralfde_DE
dc.contributor.editorZiegler, Jürgende_DE
dc.date.accessioned2017-11-20T08:44:40Z
dc.date.available2017-11-20T08:44:40Z
dc.date.issued2014-04
dc.identifier.issn2196-6826de_DE
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/6231
dc.description.abstractFostering knowledge exchange among peers is important aspect for motivation, achievement of learning goals as well as improvement of problem solving competency in elearning environments or for computer-based learning. Still, the positive effects of such an exchange depend strongly on the suitability of the selected peers in a learning group. This article describes categories of criteria to be considered by a group formation algorithm for learning groups. Additionally, existing algorithmic solutions from related work will be compared concerning several imposed requirements. For simultaneous consideration of all these requirements, the GroupAL algorithm is introduced. It supports the use of multi-dimensional criteria that are either expected to be matched homogeneous or heterogeneous among participants while aiming for equally good group formation for the whole cohort of participants to be matched. The underlying GroupAL architecture various group formation algorithms and defines a normed metric for learning group formations. This metric allows comparison of different created group formations and is robust against variations on number of used criteria or changes in the underlying cohort of participants. Finally, the presented evaluation reveals the advantages and widespread applicability of GroupAL in comparison to the investigated algorithmic solutions from related work. The approach chosen for GroupAL results in better cohort performance indices and group formation quality under the chosen conditions and with the selected data sets.de_DE
dc.language.isoende_DE
dc.publisherDe Gruyterde_DE
dc.relation.ispartofi-com: Vol. 13, No. 1de_DE
dc.subjectLerngruppende_DE
dc.subjectGruppenformationskriteriende_DE
dc.subjectKollaboratives Lernende_DE
dc.subjectOptimierungsalgorithmende_DE
dc.subjectPeer Educationde_DE
dc.titleGroupAL: ein Algorithmus zur Formation und Qualitätsbewertung von Lerngruppen in E-Learning-Szenarien / GroupAL: an algorithm for group formation and quality evaluation of learning groups in e-learning scenariosde_DE
dc.typeresearch-articlede_DE
dc.pubPlaceBerlinde_DE
mci.document.qualitydigidocde_DE
mci.reference.pages70–81de_DE
gi.identifier.doi10.1515/icom-2014-0010


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