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Computer-Based Learning: Graphical Integration of Whole and SectionalNeuroanatomy Improves Long-Term Retention

机译:基于计算机的学习:整体和局部的图形集成神经解剖学可改善长期保留

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摘要

A study was conducted to test the hypothesis that instruction with graphically integrated representations of whole and sectional neuroanatomy is especially effective for learning to recognize neural structures in sectional imagery (such as MRI images). Neuroanatomy was taught to two groups of participants using computer graphical models of the human brain. Both groups learned whole anatomy first with a three-dimensional model of the brain. One group then learned sectional anatomy using two-dimensional sectional representations, with the expectation that there would be transfer of learning from whole to sectional anatomy. The second group learned sectional anatomy by moving a virtual cutting plane through the three-dimensional model. In tests of long-term retention of sectional neuroanatomy, the group with graphically integrated representation recognized more neural structures that were known to be challenging to learn. This study demonstrates the use of graphical representation to facilitate a more elaborated (deeper) understanding of complex spatial relations.
机译:进行了一项研究,以验证以下假设:具有图形表示形式的整体和截面神经解剖结构的指令对于学习识别截面图像(例如MRI图像)中的神经结构特别有效。使用人脑的计算机图形模型向两组参与者教授了神经解剖学。两组都首先通过大脑的三维模型学习了整个解剖结构。然后,一组人使用二维截面表示来学习截面解剖,并期望将学习从整个解剖学转移到截面解剖。第二组通过在三维模型中移动虚拟切割平面来学习剖面解剖。在长期保留断层神经解剖学的测试中,具有图形集成表示的小组认识到更多的神经结构,这些神经结构已知难以学习。这项研究演示了图形表示法的使用,以促进对复杂空间关系的更详尽(更深入)的理解。

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