通过机会识别和活动衔接评价处理无缝学习中的连续性

Giuseppe D’aniello, Angelo Gaeta, F. Orciuoli, P. Rossi, S. Tomasiello
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引用次数: 4

摘要

在不同的活动和环境中处理学习经验的连续性是无缝学习的关键挑战。当前的上下文和活动识别技术在已知传感器部署和数据的固定环境中工作良好,但不能适应动态和变化的情况,例如,当学习者从密集的传感器环境移动到稀少的传感器环境时。此外,即使我们能够或多或少精确地识别活动,但是否存在与这些活动相关的有用和有趣的教育概念仍然是一个理解问题。在这篇短文中,我们讨论了我们对机会主义方法的定义的想法和初步结果,以识别在传感器丰富性和知识表达性方面利用环境特征的活动和内容。其基本思想是识别学习者所处的环境类型,然后利用环境的具体特征采用最合适的技术。接下来,我们将讨论两种测量方法,以帮助我们理解i)学习者所参与的一系列(非正式的、非正式的、正式的)活动的凝聚力程度,以及ii)学习者是否能够并且有适当的性格从活动的执行中获得新知识或发展新能力。我们提出在群体智能算法的适应度函数中采用第一种度量来优化内聚活动的搜索。
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Handling Continuity in Seamless Learning via Opportunistic Recognition and Evaluation of Activity Cohesion
Handling the continuity of learning experience across different activities and contexts is a key challenge for seamless learning. Current context and activity recognition techniques work well in fixed environments where sensors deployment and data are known but are not adaptable to dynamic and changing situations when, for instance, a learner moves from dense to rare sensor environments. Moreover, even if we are able to recognize with more or less precision activities, it still remains the issue of understanding if there are useful and interesting educational concepts related to the activities. In this short paper we discuss our ideas and preliminary results on the definition of an opportunistic approach to recognize activities and contents that leverages on the characterization of the environments in terms of sensor richness and knowledge expressiveness. The basic idea is to recognize the kind of environment in which a learner is involved and then to adapt the most suitable techniques taking advantage of the specific features of the environment. Next, we discuss two measures allowing us to understand i) the cohesion degree of the set of (informal, not formal, formal) activities a learner is involved in, and ii) if the the learner is able and in a proper disposition to acquire new knowledge or develop a new competence from the execution of activities. We propose the adoption of the first measure in the fitness function of a swarm intelligence algorithm to optimise the search of cohesive activities.
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