Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
On the Effectiveness of Web Usage Mining for Page Recommendation and Restructuring
Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors
User Modeling and User-Adapted Interaction
Some useful tactics to modify, map and mine data from intelligent tutors
Natural Language Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Data mining in course management systems: Moodle case study and tutorial
Computers & Education
Personalization in an interactive learning environment through a virtual character
Computers & Education
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
User models for adaptive hypermedia and adaptive educational systems
The adaptive web
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Hybrid web recommender systems
The adaptive web
Social navigation support in a course recommendation system
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Using rules discovery for the continuous improvement of e-learning courses
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Analysis of e-learning processes
Proceedings of the 2011 Workshop on Open Source and Design of Communication
Supporting teachers in adaptive educational systems through predictive models: A proof of concept
Expert Systems with Applications: An International Journal
Personalized Learning Course Planner with E-learning DSS using user profile
Expert Systems with Applications: An International Journal
Monitoring student progress using virtual appliances: A case study
Computers & Education
Artificial Intelligence Review
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
Clustering for improving educational process mining
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in order to help the instructor to carry out the whole Web mining process. Our objective is to be able to recommend to a student the most appropriate links/Web pages within the AHA! system to visit next. Several experiments are carried out with real data provided by Eindhoven University of Technology students in order to test both the architecture proposed and the algorithms used. Finally, we have also described the meaning of several recommendations, starting from the rules discovered by the Web mining algorithms.