Basic support for cooperative work on the World Wide Web
International Journal of Human-Computer Studies - Special issue: innovative applications of the World Wide Web
The grid
The development of behavior-based user models for a computer system
UM '99 Proceedings of the seventh international conference on User modeling
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Data mining of web access logs from an academic web site
Design and application of hybrid intelligent systems
User Navigational Behavior in e-Learning Virtual Environments
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Data & Knowledge Engineering
Enabling Efficient Real Time User Modeling in On-Line Campus
UM '07 Proceedings of the 11th international conference on User Modeling
Using Bi-clustering Algorithm for Analyzing Online Users Activity in a Virtual Campus
INCOS '10 Proceedings of the 2010 International Conference on Intelligent Networking and Collaborative Systems
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
A grid-aware implementation for providing effective feedback to on-line learning groups
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
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This paper reports on a multi-fold approach for the building of user models based on the identification of navigation patterns in a virtual campus, allowing for adapting the campus' usability to the actual learners' needs, thus resulting in a great stimulation of the learning experience. However, user modeling in this context implies a constant processing and analysis of user interaction data during long-term learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily, the massive processing is a foremost step in extracting useful information. To this end, this work studies, first, the viability of processing large log data files of a real Virtual Campus using different distributed infrastructures. More precisely, we study the time performance of massive processing of daily log files implemented following the master-slave paradigm and evaluated using Cluster Computing and PlanetLab platforms. The study reveals the complexity and challenges of massive processing in the big data era, such as the need to carefully tune the log file processing in terms of chunk log data size to be processed at slave nodes as well as the bottleneck in processing in truly geographically distributed infrastructures due to the overhead caused by the communication time among the master and slave nodes. Then, an application of the massive processing approach resulting in log data processed and stored in a well-structured format is presented. We show how to extract knowledge from the log data analysis by using the WEKA framework for data mining purposes showing its usefulness to effectively build user models in terms of identifying interesting navigation patters of on-line learners. The study is motivated and conducted in the context of the actual data logs of the Virtual Campus of the Open University of Catalonia.