Framework for Context-Aware Mobile Learning

情境感知的移动的学习是下一代智能网络学习系统的重要需求。然而，在真实的世界应用中的上下文管理是一个复杂的信息处理任务。在这项研究计划中，上下文反映了在整个企业生态系统中培养普遍学习的及时性和移动性。其目的是动态地提供有用的学习资源的需求，在任何地方，在学习者驱动的上下文中，学习者的时间表。这对企业学习提供者特别重要，以便使组织能够将学习转化为战略杠杆。随着学习材料的扩大和较长的教学时间变得普遍，知识的获取变得依赖于时间，并且由于当今的专门知识经济要求发生了相当大的变化，越来越多地在飞行中获得。然而，推理的过程中，沟通，并提出上下文感知学习资源的移动的学习者提出了新的挑战，研究界。这主要是由于移动的技术的局限性（即有限的无线网络带宽，和有限的资源上的移动的设备），并缺乏认知移动学习模型（即相关的领域知识表示和上下文信息检索，时间约束指令，人机交互的问题）。本研究计划通过利用语义网理论、电子学习标准化和移动的计算的进展，有助于解决认知移动学习中的一些具有挑战性的问题。该方法是开发一个以知识为中心的框架的基础上，结构知识到有意义的内容，并创建一个环境，软件代理漫游周围的对等学习对象库可以很容易地进行复杂的学习服务发现和组合。本体的使用旨在提供定制的学习模式，这些学习模式根据学习者的需求和可用的学习时间按需交付给学习者和/或与其他模式相结合，以形成自调整的学习路线。

Context-aware mobile learning is an important requirement for next generation intelligent e-learning systems. However, context-management in real world applications is a complex information processing task. In this research program, context reflects timeliness and mobility to nurture pervasive learning throughout an enterprise ecosystem. The aim is to dynamically provide useful learning resources on demand, anywhere, in a learner-driven context and on a learner's schedule. This is particularly important for corporate learning providers in order to make it possible for organizations to transform learning into a strategic lever. As learning material scale up and longer instruction times become common, knowledge acquisition become time-dependent and is increasingly acquired on the fly due to considerable changes in today's know-how economy requirements. However, the process of inferring, communicating, and presenting context-aware learning resources to mobile learners poses new challenges to the research community. This is mainly due to the limitations of mobile technology (i.e. limited bandwidth on wireless networks, and limited resources on mobile devices), and to the lack of cognitive mobile-learning models (i.e. problems related to domain-knowledge representation and contextual information retrieval, time constrained instruction, and human-computer interaction). This research program contributes towards solving some of the challenging problems in cognitive mobile-learning by making use of the progress in Semantic Web theory, e-learning standardization, and mobile computing. The approach is to develop a knowledge-centered framework based on ontological formulation that structures knowledge into meaningful content and creates an environment where software agents roaming around peer-to-peer learning object repositories can readily carry out sophisticated learning services discovery and compositions. The use of ontology aims at providing customized learning patterns which are delivered on-demand to the learner and/or combined with other patterns to form a self-adjusted learning route according to the learner's needs and available learning time.