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dc.contributor.authorTimm, Justin
dc.contributor.authorOtto, Benjamin
dc.contributor.authorSchramm, Thilo
dc.contributor.authorStriewe, Michael
dc.contributor.authorSchmiemann, Philipp
dc.contributor.authorGoedicke, Michael
dc.date.accessioned2020-04-22T03:52:19Z
dc.date.available2020-04-22T03:52:19Z
dc.date.issued2020
dc.identifier.issn2196-6826
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/32170
dc.description.abstractsing two case studies from biology, the article demonstrates and analyses how domain-specific self-learning items with variable content can be generated automatically for a <em>blended learning</em> environment. It shows that automated item generation works well even for highly specific technical properties and that a good item quality can be produced. Evaluations are based on sample exercises from two courses in botany and genetics, each with more than 100 participants.en
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofi-com: Vol. 19, No. 1
dc.subjectBlended Learning
dc.subjectE-Assessment
dc.subjectAutomated Item Generation
dc.subjectBiology Education
dc.titleTechnical Aspects of Automated Item Generation for Blended Learning Environments in Biology: An Analysis of Two Case Studies from the Fields of Botany and Geneticsen
dc.typeText/Journal Article
dc.pubPlaceBerlin
mci.reference.pages3-15
dc.identifier.doi10.1515/icom-2020-0001


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