The suitability of kinesthetic learning activities for teaching distributed algorithms
The suitability of kinesthetic learning activities for teaching distributed algorithms
复制标题
动觉学习活动对于分布式算法教学的适用性
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
S. M. Pike
中科院分区：
文献类型：
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作者：
P. Sivilotti;S. M. Pike
动觉学习是指学生通过主动地进行身体活动而不是被动地听讲座来学习的过程。教学研究表明，动觉学习是一种基本的、强有力的、普遍存在的学习方式。到目前为止，努力将这种学习方式纳入计算机科学课程集中在入门课程。然而，高级课程的材料也可以从类似的方法中受益。特别是，分布式计算课程，由于它们所涵盖的材料的性质，是唯一适合利用这种学习技术。我们已经开发和试点收集的动觉活动的高年级本科生或研究生水平的分布式系统课程。我们对这些练习进行了详细的描述，并讨论了促成其成功的因素。
Kinesthetic learning is a process in which students learn by actively carrying out physical activities rather than by passively listening to lectures. Pedagogical research indicates that kinesthetic learning is a fundamental, powerful, and ubiquitous learning style. To date, efforts to incorporate this learning style within the computer science curriculum have focussed on introductory courses. Material in upper-level courses, however, can also benefit from a similar approach. In particular, courses on distributed computing, by the very nature of the material they cover, are uniquely suited to exploiting this learning technique. We have developed and piloted a collection of kinesthetic activities for a senior undergraduate or graduate-level course on distributed systems. We give detailed descriptions of these exercises and discuss factors that contribute to their success.