Print this pageAnnotated Bibliography
Below you will found an annotated bibliography of Web mining in education articles (including a few essentials about Web mining in general). We will be glad to have any comment on this list.
Web Mining
Cooley, R., Mobasher, B., & Srivastava, J. (1997). Web mining: Information and pattern discovery on the World Wide Web. Paper presented at the IEEE 9th International Conference on Tools with Artificial Intelligence (ICTAI'97), Newport Beach, CA.
This paper very well presents the Web mining taxonomy by the two main categories: Web usage mining and Web content mining (the third category is Web structure mining). The authors describe the architecture of Web mining in a way which is not so technical, and demonstrate the patternd discovery and analysis processes.
Etzioni, O. (1996). The World Wide Web: quagmire or gold mine? Communications of ACM, 39(11), 65-68.
In this short article, Etzioni laid the foundations for using the term Web mining. The article starts with the statement: "Skeptics believe the Web is too unstructured for Web mining to succeed", but only a few sentences later it take a more optimistic point of view when mentioning the structured Web hypothesis: "Information on the Web is sufficiently structured to facilitate effective Web mining". Preliminary evidence is gathered in order to end the article in another promising statement: "although the Web is less structured than we might hope, it is less random than we might fear".
Kosala, R., & Blockeel, H. (2000). Web mining research: A survey. SIGKDD Explorations, 2(1), 1-15.
A detailed survey of Web mining resaerch, in which the authors present three categories of this field: Web usage mining, Web content mining, Web structure mining, and then situate some of the resaerch into those three categories.
Web Mining in Education (Mainly Introductive)
Pahl, C. (2004). Data mining technology for the evaluation of learning content interaction. International journal of E-Learning, 3(4), 47-55.
The author proposes Web mining as the central evaluation technology for the anylysis of the usage of computer-based educational environments, and in particular of the interaction with educational content (including multimedia). While customer relationship management is a central aim of e-commerce, the author suggests learner relationship management as an objective of e-learning.
Rafaeli, S., & Ravid, G. (1997). Online, Web based learning environment for an Information systems course: Access logs, linearity and performance. Paper presented at the Information Systems Education Conference, Orlando, FL.
This is one of the first attemtps of focusing on access log files for evaluating the learning process. The authors used logged data statistics and, among other findings, showed a very high positive linear correlations among the use behaviors, and significant modestly positive linear correlations between the final grade and each of the usage behavior measures.
Zaiane, O. R. (2001). Web usage mining for a better Web-based learning environment. Paper presented at the 4th IASTED International Conference on Advanced Technology for Education (CATE'01), Banff, Canada.
In this paper, the author discusses some data mining and machine learning techniques that could be used to enhance web-based learning environments for the educator to better evaluate the leaning process, as well as for the learners to help them in their learning endeavour. With the awareness of the potential advantages of integrated Web usage mining and the insufficient data recorded by Web servers - the author concludes - there is a need for more specialized logs from the application side to enrich the information already logged by the Web server.
Web Mining in Education (Research)
Hwang, W.-Y., & Wang, C.-Y. (2004). A study of learning time patterns in asynchronous learning environments. Journal of Computer Assisted Learning, 20(4), 292-304.
This paper focuses on a qualitative-like analysis of time patterns in asynchronous leaning environments. Through comparing learning time intensity, the researchers define six burst styles and three diligence styles and find correlations between some of those styles and other variables related to the online behavior (e.g., quality and quantity of interaction) and to the learning behavior (e.g., dropout, achievments)
Romero, C., & Ventura, S. (2006). Data mining in e-learning. Southampton, UK: WIT Press.
This book is a collection of articles (chapters) aiming on domnstrating how data mining can assist e-learning. Although the book lacks a comprehensive and consising agenda, the research brought within its various chapters help in understanding the big picture and the problems and issues involved in it.