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RESEARCH PRODUCT
Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow
Anis YazidiAsieh Abolpour MofradMorten GoodwinHugo Lewi HammerErik Arntzensubject
Stochastic point locationComputer scienceCognitive NeuroscienceGame ranking systemsAnalogyIntelligent tutoring system02 engineering and technologyField (computer science)Intelligent tutoring systemAdjusting delayed matching-to-sampleTask (project management)03 medical and health sciences0302 clinical medicineHuman–computer interaction0202 electrical engineering electronic engineering information engineeringStochastic point locationsVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550State of flowTrueSkillSpaced retrievalComputerized adaptive testingComputingMilieux_PERSONALCOMPUTINGIntelligent tutoring systemsOnline learning020201 artificial intelligence & image processingComputerized adaptive testingState (computer science)Adaptive task difficulties030217 neurology & neurosurgeryResearch ArticleAdaptive task difficultydescription
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. Open Access 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/.
year | journal | country | edition | language |
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2020-08-27 | Cognitive Neurodynamics |