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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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Education has been the focus of extensive debate related to design. The main reason for this controversy is information asymmetry between ‘teaching’ and ‘learning’ in design education. In other words, wording used in design education has a structure that is difficult to reproduce as images in the brains of trainees who receive them. Most design courses in design disciplines are studio-centered. In other words, a large number of participants participate in design studios and experience various shapes. The sensory meaning memory obtained from this shape experience is an implicit memory that cannot be consciously used or communicated. The purpose of this study is to identify the reasons for information asymmetry in design education and to propose ways to overcome this problem in order to have a positive impact on design education. This study focuses on the relationship between learning and memory in the context of the neural activity structure of the brain provided by neurology. First, starting from the memory structure, analyze the relationship between different memory types and learning. Afterwards, the process of memory encoding and retrieval was analyzed for its influence on learning effect. Finally, analyzing the memory styles used in existing design education, explores the impact of visual information encoded and transmitted through semantics in design on learning efficiency. Studies have shown that the information educators transmit to students is implicitly encoded in existing design education processes. However, the accuracy and communicability of content encoded through implicit memory are inevitably reduced relative to content encoded through explicit memory. Therefore, a meaningful asymmetry exists between them. Since the method of encoding visual information in language affects memory retrieval, the design knowledge generated in this way also affects its utilization. Efficiency of design education and learning can be improved by matching the encoding contexts for transmission, storage and extraction.
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