Exploiting Partial Problem Spaces Learned from Users' Interactions to Provide Key Tutoring Services in Procedural and Ill-Define

In previous works, we showed how sequential pattern mining can be used to extract a partial problem space from logged user interactions for a procedural and ill-defined domain where classic domain knowledge acquisition approaches dont work well. In this paper, we describe in details how such a problem space can support important tutoring services such as (1) recognizing the plan of a learner, (2) providing hints and (3) estimating the profile of a learner including its expertise level and missing or misunderstandood skills. Keywords. intelligent tutoring systems, domain knowledge mining, tutoring services

• Type: Journal
• Year: 2009
• Where: AIED
• Authors: Philippe Fournier-Viger, Roger Nkambou, Engelbert Mephu Nguifo
• Keywords: AIED 2009, Artificial Intelligence, Domain Knowledge, Domain Knowledge Acquisition, Problem Space