AUTOMATED CATEGORIZATION SCHEME FOR DIGITAL LIBRARIES IN DISTANCE LEARNING: A Pattern Recognition Approach

Abstract

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is therefore very important for easy and fast access to the desired information. Improper categorization of data may mislead the students searching the library. Since manual categorization of huge amount of data might be challenging, an automatic and reliable method is needed. In this sense, this paper proposes an automated categorization scheme for digital libraries in distance learning. The categorization scheme is designed and developed by a pattern recognition approach. Effectiveness of the proposed scheme is evaluated on widely used Reuters database. The results of the experimental study verify that the proposed scheme is a good candidate for categorization of digital libraries in distance learning programs.

Digital libraries play a crucial role in distance learning. Nowadays, they are one of the fundamental information sources for the students enrolled in this learning system. These libraries contain huge amount of instructional data (text, audio and video) offered by the distance learning program. Organization of the digital libraries is therefore very important for easy and fast access to the desired information. Improper categorization of data may mislead the students searching the library. Since manual categorization of huge amount of data might be challenging, an automatic and reliable method is needed. In this sense, this paper proposes an automated categorization scheme for digital libraries in distance learning. The categorization scheme is designed and developed by a pattern recognition approach. Effectiveness of the proposed scheme is evaluated on widely used Reuters database. The results of the experimental study verify that the proposed scheme is a good candidate for categorization of digital libraries in distance learning programs.