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.

本文提出了一种自适应任务难度分配方法，我们将其称为平衡难度任务查找器（BDTF）。目的是通过在学习者的技能和任务难度之间进行权衡，向学习者推荐任务，从而使学习者体验流畅的状态在学习中。当某人完全沉浸于一项活动中时，流动是一种心理状态。流动状态是一个多学科的研究领域，不仅在心理学领域，而且在神经科学，教育，体育和游戏领域都得到了研究。本文背后的想法是尝试以类似于Elo的国际象棋技巧等级（Glickman in Am Chess J 3：59–102）和TrueSkill（Herbrich等人，神经信息处理系统进展，2006）的方式实现流状态。 ）（适用于匹配的游戏玩家），其中“匹配的玩家”应具有类似的能力和技能，以保持游戏的动机和参与水平。BDTF在选择合适的对手或合适的游戏等级与自动选择学习任务的合适难度等级之间进行类比。这种方法