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dc.contributor.authorYazidi, Anis
dc.contributor.authorAbolpour Mofrad, Asieh
dc.contributor.authorGoodwin, Morten
dc.contributor.authorHammer, Hugo Lewi
dc.contributor.authorArntzen, Erik
dc.date.accessioned2020-08-28T10:37:49Z
dc.date.accessioned2020-10-16T11:58:51Z
dc.date.available2020-08-28T10:37:49Z
dc.date.available2020-10-16T11:58:51Z
dc.date.issued2020-08-27
dc.identifier.citationYazidi A, Abolpour Mofrad A, Goodwin MG, Hammer HL, Arntzen E. Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow. Cognitive Neurodynamics. 2020en
dc.identifier.issn1871-4080
dc.identifier.issn1871-4080
dc.identifier.issn1871-4099
dc.identifier.urihttps://hdl.handle.net/10642/9070
dc.description.abstractAn adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and difficulty of the tasks such that the learner experiences a state of flow during the learning. Flow is a mental state that psychologists refer to when someone is completely immersed in an activity. Flow state is a multidisciplinary field of research and has been studied not only in psychology, but also neuroscience, education, sport, and games. The idea behind this paper is to try to achieve a flow state in a similar way as Elo’s chess skill rating (Glickman in Am Chess J 3:59–102) and TrueSkill (Herbrich et al. in Advances in neural information processing systems, 2006) for matching game players, where ‘‘matched players’’ should possess similar capabilities and skills in order to maintain the level of motivation and involvement in the game. The BDTF draws analogy between choosing an appropriate opponent or appropriate game level and automatically choosing an appropriate difficulty level of a learning task. This method, as an intelligent tutoring system, could be used in a wide range of applications from online learning environments and e-learning, to learning and remembering techniques in traditional methods such as adjusting delayed matching to sample and spaced retrieval training that can be used for people with memory problems such as people with dementia.en
dc.description.sponsorshipOpen Access funding provided by OsloMet - Oslo Metropolitan University.en
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesCognitive Neurodynamics;14
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAdaptive task difficultiesen
dc.subjectIntelligent tutoring systemsen
dc.subjectGame ranking systemsen
dc.subjectOnline learningen
dc.subjectComputerized adaptive testingen
dc.subjectStochastic point locationsen
dc.titleBalanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flowen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2020-08-28T10:37:49Z
dc.description.versionpublishedVersionen
dc.identifier.doihttps://dx.doi.org/10.1007/s11571-020-09624-3
dc.identifier.cristin1825766
dc.source.journalCognitive Neurodynamics


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.