Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Abolpour Mofrad, Asieh | |
dc.contributor.author | Goodwin, Morten | |
dc.contributor.author | Hammer, Hugo Lewi | |
dc.contributor.author | Arntzen, Erik | |
dc.date.accessioned | 2020-08-28T10:37:49Z | |
dc.date.accessioned | 2020-10-16T11:58:51Z | |
dc.date.available | 2020-08-28T10:37:49Z | |
dc.date.available | 2020-10-16T11:58:51Z | |
dc.date.issued | 2020-08-27 | |
dc.identifier.citation | Yazidi 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. 2020 | en |
dc.identifier.issn | 1871-4080 | |
dc.identifier.issn | 1871-4080 | |
dc.identifier.issn | 1871-4099 | |
dc.identifier.uri | https://hdl.handle.net/10642/9070 | |
dc.description.abstract | An 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.sponsorship | Open Access funding provided by OsloMet - Oslo Metropolitan University. | en |
dc.language.iso | en | en |
dc.publisher | Springer | en |
dc.relation.ispartofseries | Cognitive Neurodynamics;14 | |
dc.rights | 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/. | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Adaptive task difficulties | en |
dc.subject | Intelligent tutoring systems | en |
dc.subject | Game ranking systems | en |
dc.subject | Online learning | en |
dc.subject | Computerized adaptive testing | en |
dc.subject | Stochastic point locations | en |
dc.title | Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow | en |
dc.type | Journal article | en |
dc.type | Peer reviewed | en |
dc.date.updated | 2020-08-28T10:37:49Z | |
dc.description.version | publishedVersion | en |
dc.identifier.doi | https://dx.doi.org/10.1007/s11571-020-09624-3 | |
dc.identifier.cristin | 1825766 | |
dc.source.journal | Cognitive Neurodynamics |
Tilhørende fil(er)
Denne innførselen finnes i følgende samling(er)
-
TKD - Institutt for informasjonsteknologi [955]
TKD - Department of Computer Science
Med mindre annet er angitt, så er denne innførselen lisensiert som 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/.