文章基本信息

标题：Searching for and positioning of contextualized learning objects
本地全文：下载
作者：Silvia Margarita Baldiris Navarro ; Sabine Graf ; Ramon Fabregat 等
期刊名称：The International Review of Research in Open and Distributed Learning
印刷版ISSN：1492-3831
出版年度：2012
卷号：13
期号：5
页码：76-101
语种：English
出版社：AU Press
摘要：Learning object economies are marketplaces for the sharing and reuse of learning objects (LO). There are many motivations for stimulating the development of the LO economy. The main reason is the possibility of providing the right content, at the right time, to the right learner according to adequate quality standards in the context of a lifelong learning process; in fact, this is also the main objective of education. However, some barriers to the development of a LO economy, such as the granularity and editability of LO, must be overcome. Furthermore, some enablers, such as learning design generation and standards usage, must be promoted in order to enhance LO economy. For this article, we introduced the integration of distributed learning object repositories (DLOR) as sources of LO that could be placed in adaptive learning designs to assist teachers’ design work. Two main issues presented as a result: how to access distributed LO, and where to place the LO in the learning design. To address these issues, we introduced two processes: LORSE, a distributed LO searching process, and LOOK, a micro context-based positioning process, respectively. Using these processes, the teachers were able to reuse LO from different sources to semi-automatically generate an adaptive learning design without leaving their virtual environment. A layered evaluation yielded good results for the process of placing learning objects from controlled learning object repositories into a learning design, and permitting educators to define different open issues that must be covered when they use uncontrolled learning object repositories for this purpose. We verified the satisfaction users had with our solution.
关键词：learning design;learning objects economy;word sense disambiguation; micro-context; similarity measures; e-learning