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dc.contributor.authorAndersen, Per-Arne
dc.contributor.authorKråkevik, Christian
dc.contributor.authorGoodwin, Morten
dc.contributor.authorYazidi, Anis
dc.date.accessioned2019-10-24T00:37:09Z
dc.date.available2019-10-24T00:37:09Z
dc.date.created2017-01-05 00:34
dc.date.issued2016-06-23
dc.identifieroai:arXiv.org:1606.07233
dc.identifierhttp://arxiv.org/abs/1606.07233
dc.identifier.urihttp://hdl.handle.net/20.500.12424/794430
dc.description.abstractWith the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized assignment of learning tasks to student so that to improve his performance in online learning environments. As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments. The SBTS is inspired by the class of multi-armed bandit algorithms. However, in contrast to standard multi-armed bandit approaches, the SBTS aims at acquiring two criteria related to student learning, namely: which topics should the student work on, and what level of difficulty should the task be. The SBTS centers on innovative reward and punishment schemes in a task and skill matrix based on the student behaviour. To verify the algorithm, the complex student behaviour is modelled using a neighbour node selection approach based on empirical estimations of a students learning curve. The algorithm is evaluated with a practical scenario from a basic java programming course. The SBTS is able to quickly and accurately adapt to the composite student competency --- even with a multitude of student models.
dc.description.abstractComment: 6th International Conference on Web Intelligence
dc.subjectComputer Science - Artificial Intelligence
dc.titleAdaptive Task Assignment in Online Learning Environments
dc.typetext
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:10388575
ge.identifier.permalinkhttps://www.globethics.net/gel/10388575
ge.lastmodificationdate2017-01-05 00:34
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid148934
ge.oai.repositoryid58
ge.oai.setnameComputer Science
ge.oai.setspeccs
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttp://arxiv.org/abs/1606.07233


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