The Clustering of Large Scale E-Learning Resources

Introduction And Related Work

With the pervasion of the Internet, e-learning is more and more popular, which provides a brand new way for people to learn without attending face-to-face class. With e-learning, the student and the teacher use online technology to interact, which profits from a combination of techniques including computer networks, multimedia, content portals, digital libraries, search engines, etc.. The worldwide e-learning industry is estimated to be worth over 38 billion euros according to conservative estimates. With the prevalence of e-learning, the amount of learning resources also grows exponentially, which makes it not feasible to access them only by clicking links. Thereby, an effective mechanism is needed to locate the resources, with which people could find the e-learning materials they want with facility. To accurately locate the e-learning materials a user is seeking for, system has to guess the user’s underlying intentions from the text typed in, rather than merely return the results from literally matching, particularly when the user is not familiar with the terminologies of the field which he/she is trying to learning. Thus, leveraging the data mining technology to locate the resources semantically related to the querying text becomes meaningful. Nowadays, the e-learning resources comprise texts, images, videos, audios and materials in other modalities, however, text materials are the best choice to be analyzed and understood, in that texts account for the biggest part and only the text resources reflect the information most directly. Besides, taking efficiency and the expensive mining cost into account, it’s reasonable to focus only on the text materials and to neglect materials in other modalities. Therefore, e-learning materials and e-learning resources in this chapter mostly mean e-learning text documents.

In data mining technology, clustering is to partition a data set into subsets, so that the data in each subset share some common trait. See (Jain et al.,1999; Xu et al., 2005) for details. Therefore, clustering is an effective method to discover clues when little is known about the data. Besides, e-learning resources are intrinsically appropriate to be clustered, in that fields of materials concerning likely overlap fields of others, and materials concerning similar fields probably use the same words, particularly the same terminologies. For example, two physics books will likely use words such as ‘energy’, ‘force’, ‘mass’, and ‘charge’ repeatedly, which consequently strengthens the correlation between the books . So we adopt the clustering method to preprocess the e-learning resources to mine the correlations among the materials.

Traditionally, measures of text similarity have been used for a long time in applications in natural language processing and related areas (Corley & Mihalcea, 2005) .One of the earliest applications of text similarity is perhaps the vector model in information retrieval, where the document most relevant to a user’s query is determined by ranking documents in a collection in reversed order of their similarity to the given query (Salton & Lest, 1968). In the vector space model, a document is represented by a vector indexed by the terms of the corpus, so two documents that use semantically related but distinct words will therefore show no similarity (Kandola et al., 2002). Many methods were proposed to explicitly or implicitly discover the similarity between different terms, such as (Landauer, Foltz & Laham, 1998; Corley & Mihalcea, 2005; Kandola et al., 2002), as well as other WordNet based methods (Budanitsky & Hirst, 2001) .