Knowledge Maps for e-Learning
Jae Hwa Lee, Aviv Segev, KAIST, Korea (South)
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Honolulu, Hawaii, USA ISBN 978-1-880094-90-7 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
Maps such as concept maps and knowledge maps are often used as learning materials. These maps have nodes and links, nodes as key concepts and links as relationships between key concepts. From a map, the user can recognize the important concepts and the relationships between them. To build concept or knowledge maps, domain experts are needed. Therefore, since these experts are hard to obtain, the cost of map creation is high. In this study, an attempt was made to automatically build a domain knowledge map for e-learning using text mining techniques. From a set of documents about a specific topic, keywords are extracted using the TF/IDF algorithm. A domain knowledge map (K-map) is based on ranking pairs of keywords according to the number of appearances in a sentence and the number of words in a sentence. K-map does not label links; instead K-map shows all sentences containing the two keywords placed at both ends of the relation chosen. Therefore, K-maps show promise as a tool for e-learning environments.
Lee, J.H. & Segev, A. (2011). Knowledge Maps for e-Learning. In C. Ho & M. Lin (Eds.), Proceedings of E-Learn 2011--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2399-2408). Honolulu, Hawaii, USA: Association for the Advancement of Computing in Education (AACE).
© 2011 Association for the Advancement of Computing in Education (AACE)