Machine Learning
Simulation and Multi-agent Environment for Aircraft Learning
AIMSA '00 Proceedings of the 9th International Conference on Artificial Intelligence: Methodology, Systems, and Applications
IDEAL: An Integrated Distributed Environment for Asynchronous Learning
DCW '00 Proceedings of the Third International Workshop on Distributed Communities on the Web
High-Level Student Modeling with Machine Learning
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Cooperative Agents to Track Learner's Cognitive Gap
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Towards Collaborative Intelligent Tutors: Automated Recognition of Users' Strategies
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Polite web-based intelligent tutors: Can they improve learning in classrooms?
Computers & Education
Plan recognition in exploratory domains
Artificial Intelligence
Using a user-interactive QA system to capture student’s interest and authority about course content
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
| Hi-index | 0.00 |
In this paper we describe the application of machine learning to the problem of constructing a student model for an intelligent tutoring system. The proposed system learns on a per student basis how long an individual student requires to solve the problem presented by the tutor. This model of relative problem difficulty is learned within a "two-phase" learning algorithm. First, data from the entire student population are used to train a neural network. Second, the system learns how to modify the neural network's output to better fit each individual student's performance. Both components of the model proved useful in improving its accuracy. This model of time to solve a problem is used by the tutor to control the complexity of problems presented to the student.