Framework for Context-Aware Mobile Learning

上下文感知移动学习是下一代智能电子学习系统的重要要求。然而，现实世界应用程序中的上下文管理是一项复杂的信息处理任务。在这个研究项目中，环境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。其目的是在学习者驱动的环境中，根据学习者的时间表，随时随地动态地提供有用的学习资源。这对于企业学习提供者来说尤其重要，因为它可以使组织将学习转化为战略杠杆。随着学习材料的增加和教学时间的延长，知识的获取变得依赖于时间，并且由于当今知识经济要求的巨大变化，知识的获取越来越多地在飞行中获得。然而，推断、交流和向移动学习者呈现情境感知学习资源的过程对研究界提出了新的挑战。这主要是由于移动技术的局限性（即无线网络的带宽有限，移动设备上的资源有限），以及缺乏认知移动学习模型（即与领域知识表示和上下文信息检索，时间约束指令和人机交互相关的问题）。本研究计划利用语义网理论、电子学习标准化和移动计算方面的进展，致力于解决认知移动学习中一些具有挑战性的问题。该方法是开发一个基于本体公式的以知识为中心的框架，该框架将知识结构成有意义的内容，并创建一个环境，在该环境中，围绕点对点学习对象库漫游的软件代理可以轻松地执行复杂的学习服务发现和组合。使用本体的目的是提供定制的学习模式，这些模式可以按需交付给学习者，也可以与其他模式相结合，根据学习者的需要和可用的学习时间，形成自我调整的学习路线。

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.