GVIS:用于自适应混搭来自不同来源的用户数据的集成基础设施

Luca Mazzola, Riccardo Mazza
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摘要

在本文中,我们将介绍一种基础结构,用于创建用户概要文件的混合可视化表示,将来自不同来源的数据组合在一起。我们在终身学习的背景下探索了这种方法,在终身学习中,不同的平台或服务通常用于支持学习过程。该系统是高度可配置和自适应的:数据源、数据聚合和可视化可以由管理用户动态配置,而无需更改软件的任何部分,并且具有基于有关用户或系统特征的条件的线性组合的自适应行为。生成的可视化概要文件可以采用不同的图形格式,并可以绑定到不同的数据,自动适应个人偏好、知识和上下文。我们将基础设施应用于一组联邦学习管理系统,从不同的来源检索信息,并创建学习活动的一些指示器。我们开发的软件为学习者提供了学习状态的自适应指标,并允许教师监控学习者的进度。
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GVIS: An Integrating Infrastructure for Adaptively Mashing up User Data from Different Sources
In this article we present an infrastructure for creating mash up visual representations of the user profile that combines data from different sources. We explored this approach in the context of Life Long Learning, where different platforms or services are often used to support the learning process. The system is highly configurable and adaptive: data sources, data aggregations, and visualizations can be configured on the fly by the administrative user without changing any part of the software, and have an adaptive behavior based on linear combination of conditions about user or system characteristics. The visual profiles produced can assume different graphical formats and can be bound to different data, automatically adapting to personal preferences, knowledge, and contexts. We applied our infrastructure to a set of federated Learning Management Systems, retrieving information from different sources and creating some indicators of the learning activity. The software we developed provides learners with adaptive indicators of the learning state, and allows instructors to monitor the progress of their learners.
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