Amirah Ismail

Search engines evaluation using precision and document-overlap measurements at 10-50 cutoff points

The Internet has become a huge store of distributed documents. A user of the Internet, at times, seeks information which he may not know to solve a problem. He therefore has to express his information needs as a request for information in one form or another using a search engine. The search engine then tries to infer and retrieve relevant documents and presents the results in a hit list. But, the relevant documents from the hit list can only be determined by the user. The quality of hit lists very depending on the effectiveness of the indexing process which generate the surrogates from the original documents. Usually, the quality of the hit list can be measured by the precision measure, i.e. the ratio of the number of retrieved and relevant documents over the number of retrieved documents. This measure has been used to evaluate ten major search engines using ten queries at cutoff points of 10, 20, 30, 40 and 50. We have also introduced an overlap measure to determine the commonality of documents between the hit lists of various search engines. With these two measures we can evaluate the performance of the search engines. The search engines chosen for study are Altavista, Hotbot, Excite, Lycos, Webcrawler, Infoseek, Magellan, Northernlight, SavvySearch and Metacrawler.

User-enjoyable learning environment based on Gamification elements

The present conditions of human life are more comfortable than that in the past. A huge number of modern technologies are available today because of the rapid development of information technology, which contributes to the flourishing of human life. Such modern technologies have led to the creation of Gamification, which is a new approach in information technology. In this paper we review the literature on Gamification from different perspectives and then explore the key elements of Gamification that positively affect learning when applied to any application. In addition, we discuss how the effectiveness is influenced when the implementation of Gamification in enterprises is stopped. Finally, we present an overall figure to illustrate all of the elements of Gamification according to previous research and consequently make the learning more effective as well as build an enjoyable learning environment for students.

Semantic searches for extracting similarities in a content management system

Recent content management systems have restricted means for organizing and inferring documents although much of an organizations knowledge can be created in text repositories. In the Semantic Web search emergence, inferring and understanding can be deal by ontology-based semantic mark-up and metadata management. Whilst in the educational domain, learning objects are a fundamental resource. Literally, Content Management Systems and repositories have restricted the means for organising and understanding the captured semantic relationships between the learning objects and other stored documents. To cater this situation, we propose the application of metametadata as a useful semantic based approach to address similarities in a domain to gather definite requirements. This paper focuses on the existing approaches for describing semantic relationships in Content Management Systems and how metametadata capture the pedagogic information which can be applied to enhance the semantic information stored within such a Content Management Systems or repository. It is understood that there is still lacking approaches to address similarities in a domain that meets certain requirements but the progress for the ongoing research in the area is active and shows potential advancement.

A metametadata taxonomy to support semantic searching algorithms in metadata repository

This paper discusses on a novel method for semantic searching and retrieval of information about learning materials. A novel metametadata taxonomy has been developed which provides the basis for a semantic search engine to extract, match and map queries to retrieve relevant results. Metametadata encapsulate metadata instances by using the properties and attributes provided by ontologies rather than describing learning objects. The use of ontological views assists the pedagogical content of metadata extracted from learning objects by using the control vocabularies as identified from the metametadata taxonomy. The use of ontological approach and metametadata (based on the metametadata taxonomy) have contributed towards a novel semantic searching mechanism. These three strands - the taxonomy, the ontological views, and the search algorithm - are integrated into an architecture (OMESCOD) to support the semantic search within the metadata repository.