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Learning styles and cognitive traits - Their relationship and its benefits in web-based educational systems

机译:学习方式和认知特征-他们的关系及其在基于网络的教育系统中的好处

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Different learners have different needs; they differ, for example, in their learning goals, their prior knowledge, their learning styles, and their cognitive abilities. Adaptive web-based educational systems aim to cater individual learners by customizing courses to suit their needs. In this paper, we investigate the benefits of incorporating learning styles and cognitive traits in web-based educational systems. Adaptivity aspects based on cognitive traits and learning styles enrich each other, enabling systems to provide learners with courses which fit their needs more accurately. Furthermore, consideration of learning styles and cognitive traits can contribute to more accurate student modelling. In this paper, the relationship between learning styles, in particular the Felder-Silverman learning style model (FSLSM), and working memory capacity, a cognitive trait, is investigated. For adaptive educational systems that consider either only learning styles or only cognitive traits, the additional information can be used to provide more holistic adaptivity. For systems that already incorporate both learning styles and cognitive traits, the relationship can be used to improve the detection process of both by including the additional information of learning style into the detection process of cognitive traits and vice versa. This leads to a more reliable student model.
机译:不同的学习者有不同的需求;他们在学习目标,先验知识,学习方式和认知能力等方面有所不同。基于网络的自适应教育系统旨在通过定制课程来满足他们的需求,从而满足他们的需求。在本文中,我们研究了将学习风格和认知特征纳入基于Web的教育系统中的好处。基于认知特征和学习风格的适应性方面相互补充,使系统能够为学习者提供更准确地满足其需求的课程。此外,学习风格和认知特征的考虑可以有助于更准确的学生建模。本文研究了学习风格之间的关系,特别是Felder-Silverman学习风格模型(FSLSM)与工作记忆能力(一种认知特质)之间的关系。对于只考虑学习方式或只考虑认知特征的适应性教育系统,可以使用附加信息来提供更全面的适应性。对于已经结合了学习风格和认知特征的系统,可以通过将学习风格的附加信息包括在认知特征的检测过程中来改善两者的检测过程,反之亦然。这导致了更可靠的学生模​​型。

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