Authoring Constraint-Based Tutors in ASPIRE

Abstract

This paper presents a project the goal of which is to develop ASPIRE, a complete authoring and deployment environment for constraintbased intelligent tutoring systems (ITSs). ASPIRE is based on our previous work on constraint-based tutors and WETAS, the tutoring shell. ASPIRE consists of the authoring server (ASPIRE-Author), which enables domain experts to easily develop new constraint-base tutors, and a tutoring server (ASPIRE-Tutor), which deploys the developed systems. Preliminary evaluation shows that ASPIRE is successful in producing domain models, but more thorough evaluation is planned.

A Tablet Computer-Assisted Motor and Language Skills Training Program to Promote Communication Development in Children with Autism: Development and Pilot Study

“…Customisation of the user interface to improve learning has been shown to be a useful strategy for motivation (Cha et al, 2006).…”

Learning task models in ill-defined domain using an hybrid knowledge discovery framework

“…However cognitive task analysis is a very time-consuming process [2] and cannot be applied easily for an ill-defined domain. Constraint based modeling was proposed as an alternative [3]. It consists of specifying sets of constraints on what is a correct behavior, instead of providing a complete task description.…”

“…Those domains are said to be ''ill-defined" and are characterized by the fact that for a given task there might be many alternative solution-strategies and that, for each of them, a wide spectrum of solutions can be created. According to Aleven et al [1], ill-defined domains present a number of unique challenges for researchers in Intelligent Tutoring Systems and Computer Modeling, such as ''(1) defining a viable computational model for aspects of underspecified or open-ended domains; (2) developing feasible strategies for search and inference in such domains; (3) providing feedback when the problem-solving model is not definitive; (4) structuring of learning experiences in the absence of a clear problem, strategy, and answer; (5) user models that accommodate the uncertainty of ill-defined domains; and 6) user interface design for ITSs in ill-defined domains where usually the learner needs to be creative in his actions, but the system still has to be able to analyze them". The method of cognitive task analysis that aims at producing effective problem spaces or task models by observing expert and novice users at workplace is a good solution for capturing different way of solving problems.…”

Statistical Techniques to Explore the Quality of Constraints in Constraint-Based Modeling Environments

“…This set is the most important element of this modeling paradigm in order to conduct intelligent tutorial actions. Designing the constraints set in new learning environments can be performed very easily using authoring tools such as ASPIRE (Mitrovic et al 2006) as no programming skills are needed. What is necessary to appropriately model constraints is a broad knowledge of the domain matter; the same as with any other approach when a new learning environment is going to be developed.…”