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CoALA: Contextualization Framework for Smart Learning Analytics

机译:煤炭:智能学习分析的情境化框架

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Learning Analytics (LA) has become a prominent paradigm in the context of education lately which adopts the recent advancements of technology such as cloud computing, big data processing, and Internet of Things. LA also requires an intensive amount of processing resources to generate relevant analytical results. However, the traditional approaches have been inefficient at tackling LA challenges such as real-time, high performance, and scalable processing of heterogeneous datasets and streaming data. An Internet of Things (IoT) scalable, distributed and high performance framework has the potential to address mentioned LA challenges by efficient contextualization of data. In this paper, CoALA, a Smart Learning Analytics conceptual model is proposed to improve the effectiveness of LA by utilizing an IoT-based contextualization framework in terms of performance, scalability, and efficiency.
机译:学习分析(LA)最近在教育背景下成为一个突出的范式,这采用了云计算,大数据处理和事物互联网等技术的最新进步。 LA还需要一系列密集的加工资源来产生相关的分析结果。然而,传统方法在解决洛杉矶挑战之类的诸如实时,高性能和异构数据集的可扩展处理之类的挑战方面效率低下。物联网(物联网)可扩展,分布式和高性能框架具有通过高效的数据的上下文化来解决提到的洛杉矶挑战。在本文中,提出了一种智能学习分析概念模型,以通过利用基于物联网的上下文化框架来提高LA的有效性,可扩展性和效率。

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