TITLE:
Dominant Meaning Method for Intelligent Topic-Based Information Agent towards More Flexible MOOCs
AUTHORS:
Mohammed Abdel Razek
KEYWORDS:
Massive Open Online Courses, MOOCs, Search Engine, Hypermedia Systems, Web-Based Services, Query Expansion, Probabilistic Model, Information Retrieval, Search Engine
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.6 No.4,
November
25,
2014
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.