Adaptive learning systems in the World Wide Web
UM '99 Proceedings of the seventh international conference on User modeling
Motivation and failure in educational simulation design
Smart machines in education
Growth and maturity of intelligent tutoring systems: a status report
Smart machines in education
User Modeling and User-Adapted Interaction
CHEOPS: Adaptive Hypermedia on World Wide Web
IDMS '97 Proceedings of the 4th International Workshop on Interactive Distributed Multimedia Systems and Telecommunication Services
PAMR: A Process Algebra for the Management of Resources in Concurrent Systems
FORTE '01 Proceedings of the IFIP TC6/WG6.1 - 21st International Conference on Formal Techniques for Networked and Distributed Systems
Adaptation Control in Adaptive Hypermedia Systems
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
An Intelligent Tutor for a Web-Based Chess Course
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Including Malicious Agents into a Collaborative Learning Environment
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
ELM-ART: An Intelligent Tutoring System on World Wide Web
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Curriculum Evaluation: A Case Study
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Market-Based Adaptive Discussion Forums
Advanced Internet Based Systems and Applications
An intelligent tutoring system architecture for competency-based learning
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
An interactive functional programming tutor
Proceedings of the 17th ACM annual conference on Innovation and technology in computer science education
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In this paper we introduce WHAT, an intelligent tutor for learning the functional programming language Haskell. WHAT adapts its behavior not only individually for each student but also by considering the performance of similar students. The core of its adaptive part is based on the classification of students into classes (groups of students sharing some attributes). By doing that, the behavior of past students of the same class determines how WHAT interacts, in the future, with students of that class. That is, WHAT learns how to deal with each type of student. Besides, the general model of each class is instantiated for each student in order to better fit the particular learning needs.