An Overview of Learning Object Repositories

An Overview of Learning Object Repositories

Agiris Tzikopoulos, Nikos Manouselis, Riina Vuorikari
Copyright: © 2009 |Pages: 10
DOI: 10.4018/978-1-60566-098-1.ch003
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Abstract

Learning objects are systematically organised and classified in online databases, which are termed learning object repositories (LORs). Currently, a rich variety of LORs is operating online, offering access to wide collections of learning objects. These LORs cover various educational levels and topics, and are developed by using a variety of different technologies. They store learning objects and/or their associated metadata descriptions, as well as offer a range of services that may vary from advanced search and retrieval of learning objects to intellectual property rights (IPR) management. Until now, there has not been a comprehensive study of existing LORs that will give an outline of their overall characteristics. For this purpose, this chapter presents the initial results from a survey of 59 well-known repositories with learning resources. The most important characteristics of surveyed LORs are examined and useful conclusions about their current status of development are made. A discussion of future trends in the LORs field is also carried out.
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Introduction

Data warehousing has become an important technology to integrate data sources in recent decades which enables knowledge workers (executives, managers, and analysts) to make better and faster decisions (SCN Education, 2001). From a technological perspective, Wal-Mart, as a pioneer in adopting data warehousing technology, has always adopted new technology quickly and successfully. A study of the applications and issues of data warehousing in the retailing industry based on Wal-Mart is launched. By investigating the Wal-Mart data warehouse from various perspectives, we review some of the critical areas which are crucial to the implementation of a data warehouse. In this chapter, the development, implementation, and evaluation of the Wal-Mart data warehouse is described, together with an assessment of the factors responsible for deployment of a successful data warehouse.

Data Warehousing

Data warehouse is a subject-oriented, integrated, time-variant, non-updatable collection of data used in support of management decision-making (Agosta, 2000). According to Anahory and Murray (1997), “a data warehouse is the data (meta/fact/dimension/aggregation) and the process managers (load/warehouse/query) that make information available, enabling people to make informed decisions”. Before the use of data warehouse, companies used to store data in separate databases, each of which were meant for different functions. These databases extracted useful information, but no analyses were carried out with the data. Since company databases held large volumes of data, the output of queries often listed out a lot of data, making manual data analyses hard to carry out. To resolve this problem, the technique of data warehousing was invented. The concept of data warehousing is simple. Data from several existing systems is extracted at periodic intervals, translated into the format required by the data warehouse, and loaded into the data warehouse. Data in the warehouse may be of three forms — detailed information (fact tables), summarized information, and metadata (i.e., description of the data). Data is constantly transformed from one form to another in the data warehouse. Dedicated decision support system is connected with the data warehouse, and it can retrieve required data for analysis. Summarized data are presented to managers, helping them to make strategic decisions. For example, graphs showing sales volumes of different products over a particular period can be generated by the decision support system. Based on those graphs, managers may ask several questions. To answer these questions, it may be necessary to query the data warehouse and obtain supporting detailed information. Based on the summarized and detailed information, the managers can take a decision on altering the production volume of different products to meet expected demands. The major processes that control the data flow and the types of data in the data warehouse are depicted in Figure 1. For a more detailed description of the architecture and functionalities of a data warehouse, the interested reader may refer to Inmon and Inmon (2002) and Kimball and Ross (2002).

Figure 1.

Process diagram of a data warehouse (adapted from Anahory and Murray [1997])

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