Ubiquitous learning analytics in authentic real-world scenarios

移动和传感器技术的最新进展已经开始引起许多研究，这些研究利用学习者的位置、环境、邻近和情境来实现学习过程的情境化。这些无处不在的场景中的适应性和个性化被赋予了新的意义，通过将学习者附近可用的物理对象与实时虚拟信息无缝集成来带来真实性。这种环境不仅通过扩大那些无法进入实体教室的人的机会来打破教育障碍，而且通过整合多种教学来源、情景和实时位置感知学习来增加教学的丰富性，从而克服课堂学习的局限性。典型的学习分析方法在不同但相关的粒度水平上分析学习模式，在这种无处不在的环境中使用非常有限。现有的学习分析技术和方法用于跟踪学习者为实现其学习目标和结果而进行的学习过程，特别是在在线学习环境中，但在无处不在的环境中，学习者及其环境的数据来自不同的来源，具有不同程度的可靠性和精确度，需要将实时数据与历史数据集合成，以便确定具有背景意义的模式。无处不在的学习分析填补了这一空白，将学习分析带入下一阶段，学习不限于正规环境；相反，它将正规环境与真实环境中的即时机会和学习者参与的非正式社交活动的好处相结合。本研究的主要目标是通过探索各种方法来观察和分析泛在环境中的学习过程，为学习者提供丰富的学习体验，并创建泛在学习分析模型和模块，利用位置、设备和用户模型参数的优势，并将其与实时真实的学习环境相结合，实现实时多媒体丰富的物理和虚拟学习内容的自适应交付。

Recent advances in mobile and sensor technologies have started to give rise to a lot of research that takes advantage of learners' location, environment, proximity and situation to contextualize the learning process. The adaptivity and personalization in these ubiquitous scenarios have taken a new meaning by bringing authenticity through seamless integration of physical objects available in the learner's vicinity with virtual information in real-time. Such environments not only break the barriers for education by widening the access to those who cannot come to a physical classroom but also increase the richness of the instruction by integrating multiple sources of instruction, contextualization and real-time location-aware learning, hence overcoming the limitations of classroom learning. The approaches for typical learning analytics, that analyse learning patterns in various yet related levels of granularity, have very restricted use in such ubiquitous environments. The technologies and methodologies that currently exists for learning analytics to track the study processes learners undertake to achieve their learning goals and outcomes, particularly in online learning environments, fall short in ubiquitous environments where the data about the learners and their environment comes from disparate sources, with varying levels of reliability and precision, and require synthesis of real-time data with the historical datasets in order to identify contextually meaningful patterns. Ubiquitous learning analytics fills this gap by taking learning analytics to the next stage where learning is not restricted to formal environments; rather it integrates formal environments with the benefits of just-in-time opportunities in authentic environments and the informal social interaction learners engage in. Primary goal of this research is to provide rich learning experiences to the learners by exploring various ways to observe and analyse learning process in ubiquitous environments, and create ubiquitous learning analytics models and modules that exploit the benefits of location, device and user model parameters, and combine them with real-time authentic context of learning to achieve adaptive delivery of just-in-time multimedia-rich combination of physical and virtual learning content.