Exploiting partial problem spaces learned from users' interactions to provide key tutoring services in procedural and Ill-defined domains

Abstract

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 don't 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. © 2009 The authors and IOS Press. All rights reserved.