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Learners#039; Working Memory Capacity Modeling Based on Fuzzy Logic

机译:基于模糊逻辑的学习者工作记忆能力建模

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Recently, many works have investigated the identification of learner's working memory capacity (WMC) from his/her behavior in learning systems. These works used a mechanism, called dichotomic node network, able to create representation of the learner's WMC taking into account different theories from the literature. However, this mechanism could only provide a binary representation of learner's WMC (High or Low), whereas the WMC is estimated by 7 (plus or minus two) items. This paper presents an alternative based on fuzzy logic for precisely estimate the WMC of the learner while using learning system or playing educational games. The proposed approach can improve the accuracy of learner model and then enable the fine grained recommendations that positively affect the learners' learning.
机译:近来,许多作品研究了根据学习者在学习系统中的行为来识别学习者的工作记忆能力(WMC)。这些作品使用一种称为二分节点网络的机制,该机制能够考虑到与文献不同的理论来创建学习者的WMC的表示形式。但是,此机制只能提供学习者的WMC(高或低)的二进制表示,而WMC则由7个(正负两个)项估计。本文提出了一种基于模糊逻辑的替代方案,用于在使用学习系统或玩教育游戏时精确估算学习者的WMC。所提出的方法可以提高学习者模型的准确性,然后实现对学习者的学习产生积极影响的细粒度建议。

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