Web data mining in education: Decision support by learning analytics with bloom's taxonomy

Wing Shui NG

Research output: Chapter in Book/Report/Conference proceedingChapters

2 Citations (Scopus)

Abstract

Web data mining for extracting meaningful information from large amount of web data has been exploredover a decade. The concepts and techniques have been borrowed into the education sector and the newresearch discipline of learning analytics has emerged. With the development of web technologies, it hasbeen a common practice to design online collaborative learning activities to enhance learning. To applylearning analytics techniques to monitor the online collaborative process enables a lecturer to makeinstant and informed pedagogical decisions. However, it is still a challenge to build strong connectionbetween learning analytics and learning science for understanding cognitive progression in learning.In this connection, this chapter reports a study to apply learning analytics techniques in the aspect ofweb usage mining and clustering analysis with underpinning Bloom's taxonomy to analyze students'performance in the online collaborative learning process. The impacts of intermediate interventionsare also elaborated. Copyright © 2017 by IGI Global. All rights reserved.
Original languageEnglish
Title of host publicationWeb data mining and the development of knowledge-based decision support systems
EditorsG. SREEDHAR
PublisherIGI Global
Pages58-77
ISBN (Print)9781522518785, 1522518770, 9781522518778
DOIs
Publication statusPublished - 2017

Citation

Ng, W. S. (2017). Web data mining in education: Decision support by learning analytics with bloom's taxonomy. In G. Sreedhar (Ed), Web data mining and the development of knowledge-based decision support systems (pp. 58-77). Hershey, Pennsylvania : IGI Global

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