Extending Domain Theories: Two Case Studies in Student Modeling
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Refinement-based student modeling and automated bug library construction
Journal of Artificial Intelligence in Education
Multistrategy Discovery and Detection of Novice Programmer Errors
Machine Learning - Special issue on multistrategy learning
Data mining: concepts and techniques
Data mining: concepts and techniques
A Belief Net Backbone for Student Modelling
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
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
Adaptive testing for hierarchical student models
User Modeling and User-Adapted Interaction
Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
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When a quantitative student model is constructed, one of the first tasks to perform is to identify the domain concepts assessed In general, this task is easily done by the domain experts In addition, the model may include some misconceptions which are also identified by these experts Identifying these misconceptions is a difficult task, however, and one which requires considerable previous experience with the students In fact, sometimes it is difficult to relate these misconceptions to the elements in the knowledge diagnostic system which feeds the student model In this paper we present a data-driven technique which aims to help elicit the domain misconceptions It also aims to relate these misconceptions with the assessment activities (e.g exercises, problems or test questions), which assess the subject in question.