Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs

M. A. Razek
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引用次数: 0

Abstract

The use of agent technology in a dynamic environment is rapidly growing as one of the powerful technologies and the need to provide the benefits of the Intelligent Information Agent technique to massive open online courses, is very important from various aspects including the rapid growing of MOOCs environments, and the focusing more on static information than on updated information. One of the main problems in such environment is updating the information to the needs of the student who interacts at each moment. Using such technology can ensure more flexible information, lower waste time and hence higher earnings in learning. This paper presents Intelligent Topic-Based Information Agent to offer an updated knowledge including various types of resource for students. Using dominant meaning method, the agent searches the Internet, controls the metadata coming from the Internet, filters and shows them into a categorized content lists. There are two experiments conducted on the Intelligent Topic-Based Information Agent: one measures the improvement in the retrieval effectiveness and the other measures the impact of the agent on the learning. The experiment results indicate that our methodology to expand the query yields a considerable improvement in the retrieval effectiveness in all categories of Google Web Search API. On the other hand, there is a positive impact on the performance of learning session.
面向更灵活mooc的基于主题的智能信息代理主导意义方法
agent技术作为一种强大的技术在动态环境中的应用正在迅速发展,从各个方面来看,将智能信息agent技术的优势提供给大规模在线开放课程是非常重要的,包括mooc环境的快速增长,以及对静态信息的关注多于对更新信息的关注。在这样的环境中,一个主要的问题是如何根据每时每刻都在互动的学生的需要来更新信息。使用这种技术可以确保更灵活的信息,减少浪费的时间,从而提高学习收益。本文提出了基于主题的智能信息代理,为学生提供包括各类资源在内的知识更新。智能体利用支配意义法对互联网进行搜索,对来自互联网的元数据进行控制,过滤并显示为分类的内容列表。对基于主题的智能信息代理进行了两个实验,一个是测量检索效率的提高,另一个是测量代理对学习的影响。实验结果表明,我们的扩展查询方法在b谷歌Web搜索API的所有类别的检索效率上都有很大的提高。另一方面,对学习过程的表现有积极的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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