Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The Logic-ITA in the Classroom: A Medium Scale Experiment
International Journal of Artificial Intelligence in Education
Some useful tactics to modify, map and mine data from intelligent tutors
Natural Language Engineering
Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Using Simulation to Evaluate Data-Driven Agents
Multi-Agent-Based Simulation IX
I learn from you, you learn from me: How to make iList learn from students
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Towards virtual course evaluation using web intelligence
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Normalised support: a virtual angle of measurement of 'interestingness'
International Journal of Data Analysis Techniques and Strategies
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning
Adaptation in educational hypermedia based on the classification of the user profile
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A neuro-fuzzy approach in the classification of students' academic performance
Computational Intelligence and Neuroscience
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In this paper, we show how using data mining algorithms can help discovering pedagogically relevant knowledge contained in databases obtained from Web-based educational systems. These findings can be used both to help teachers with managing their class, understand their students' learning and reflect on their teaching and to support learner reflection and provide proactive feedback to learners.