首页> 外文OA文献 >User context and personalized learning: a federation of contextualized attention metadata
【2h】

User context and personalized learning: a federation of contextualized attention metadata

机译:用户上下文和个性化学习:上下文关注元数据的联合

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

Nowadays, personalized education is a very hot topic in technology enhanced learning (TEL) research. To support students during their learning process, the first step consists in capturing the context in which they evolve. Users typically operate in a heterogeneous environment when learning, including learning tools such as Learning Management Systems and non-learning tools and services such as e-mails, instant messaging, or web pages. Thus, user attention in a given context defines the Contextualized Attention Metadata (CAM). Various initiatives and projects allow capturing CAMs in a knowledge workers’ environment not only in the TEL area, but also in other domains like Knowledge Work Support, Personal Information Management and Information Retrieval. After reviewing main existing approaches according to some specific criteria that are of main interest for capturing and sharing user contexts, we present in this paper a framework able to gather CAMs produced by any tool or computer system. The framework is built on the Web-Based Enterprise Management (WBEM) standard dedicated to system, network and application management. Attention information specific to heterogeneous tools are represented as a unified and extensible structure, and stored into a central repository compliant with the above-mentioned standard. To facilitate access to this attention repository, we introduced a middleware layer composed of two dynamic services: the first service allows users to define the attention data they want to collect, whereas the second service is dedicated to receive and retrieve the traces produced by computer systems. An implementation for collecting and storing CAM data generated by the Ariadne Finder and Moodle validates our approach.
机译:如今,个性化教育已成为技术增强学习(TEL)研究中的一个非常热门的话题。为了在学习过程中为学生提供支持,第一步是捕捉他们发展所处的环境。用户在学习时通常会在异构环境中操作,包括学习工具(如学习管理系统)和非学习工具及服务(如电子邮件,即时消息或网页)。因此,在给定上下文中的用户注意力定义了上下文化注意元数据(CAM)。各种计划和项目不仅可以在TEL领域的知识工作者环境中捕获CAM,而且还可以在其他领域(如知识工作支持,个人信息管理和信息检索)中捕获CAM。在根据捕获和共享用户上下文最感兴趣的一些特定标准回顾了主要的现有方法之后,我们在本文中提出了一个能够收集由任何工具或计算机系统生成的CAM的框架。该框架基于专用于系统,网络和应用程序管理的基于Web的企业管理(WBEM)标准构建。异构工具特有的注意信息以统一且可扩展​​的结构表示,并存储在符合上述标准的中央存储库中。为了方便访问此注意力库,我们引入了一个由两个动态服务组成的中间件层:第一个服务允许用户定义他们要收集的注意力数据,而第二个服务专用于接收和检索计算机系统产生的跟踪。收集和存储由Ariadne Finder和Moodle生成的CAM数据的实现验证了我们的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号