This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Multiparadigm Intelligent Tutoring System for Robotic Arm Training
Oct.-Dec. 2013 (vol. 6 no. 4)
pp. 364-377
Philippe Fournier-Viger, University of Moncton, Moncton
Roger Nkambou, University of Quebec in Montreal, Montreal
Engelbert Mephu Nguifo, Clermont Université, Université Blaise Pascal, Clermont-Ferrand
Andre Mayers, University of Sherbrooke, Sherbrooke
Usef Faghihi, Sull Ross State University, Texas
To assist learners during problem-solving activities, an intelligent tutoring system (ITS) has to be equipped with domain knowledge that can support appropriate tutoring services. Providing domain knowledge is usually done by adopting one of the following paradigms: building a cognitive model, specifying constraints, integrating an expert system, and using data mining algorithms to learn domain knowledge. However, for some ill-defined domains, each single paradigm may present some advantages and limitations in terms of the required resources for deploying it, and tutoring support that can be offered. To address this issue, we propose using a multiparadigm approach. In this paper, we explain how we have applied this idea in CanadarmTutor, an ITS for learning to operate the Canadarm2 robotic arm. To support tutoring services in this ill-defined domain, we have developed a multiparadigm model combining: 1) a cognitive model to cover well-defined parts of the task and spatial reasoning, 2) a data mining approach for automatically building a task model from user solutions for ill-defined parts of the task, and 3) a 3D path-planner to cover other parts of the task for which no user data are available. The multiparadigm version of CanadarmTutor allows providing a richer set of tutoring services than what could be offered with previous single paradigm versions of CanadarmTutor.
Index Terms:
Expert systems,Learning systems,Computer aided diagnosis,Problem-solving,Training,Data models,tutoring feedback,Computer-assisted instruction,intelligent tutoring systems,ill-defined domains
Citation:
Philippe Fournier-Viger, Roger Nkambou, Engelbert Mephu Nguifo, Andre Mayers, Usef Faghihi, "A Multiparadigm Intelligent Tutoring System for Robotic Arm Training," IEEE Transactions on Learning Technologies, vol. 6, no. 4, pp. 364-377, Oct.-Dec. 2013, doi:10.1109/TLT.2013.27
Usage of this product signifies your acceptance of the Terms of Use.