Web Mining For Self-directed E-learning

TL;DR: This chapter investigates specific examples of selfdirected e-learning and how their functionality and utility can be improved through the use of Web Mining technology, techniques, and practices and demonstrates the usefulness of Web mining as it applies to self-directed elearning and the need to map implicit relationships in learner behaviour, usage, and context.

Abstract: Self-directed e-learning focuses on the independent learner, one who engages in education at his own pace, free from curricular obligation. A number of tools, some purposefully and others serendipitously, have become key enablers of this learning paradigm. For example, tools such a Google Scholar, CiteSeer Research Index, etc. make it possible to do literature search without stepping out of one’s room. Due to the same technologies which helped make self-directed e-learning possible in the first place, these tools are in danger of delivering diminishing returns as micro-learning, lifelong education, and continuous education become the norm in our Information Age. Web Mining, however, may potentially offer a solution to this issue. In this chapter, we investigate specific examples of selfdirected e-learning and how their functionality and utility can be improved through the use of Web Mining technology, techniques, and practices. Our work demonstrates the usefulness of Web Mining as it applies to self-directed elearning and the need to map implicit relationships in learner behaviour, usage, and context.