Conference
Intelligent Tutoring Systems
About: Intelligent Tutoring Systems is an academic conference. The conference publishes majorly in the area(s): Intelligent tutoring system & TUTOR. Over the lifetime, 1598 publications have been published by the conference receiving 30432 citations.
Topics: Intelligent tutoring system, TUTOR, Learning environment, Collaborative learning, Educational technology
Papers published on a yearly basis
Papers
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12 Jun 1996
TL;DR: The system ELM-ART is presented which is a WWW-based ITS to support learning programming in Lisp and demonstrates how several known ITS technologies can be implemented in WWW context.
Abstract: Making ITS available on the World Wide Web (WWW) is a way to integrate the flexibility and intelligence of ITS with world-wide availability of WWW applications This paper discusses the problems of developing WWW-available ITS and, in particular, the problem of porting existing ITS to a WWW platform We present the system ELM-ART which is a WWW-based ITS to support learning programming in Lisp ELM-ART demonstrates how several known ITS technologies can be implemented in WWW context
578 citations
26 Jun 2006
TL;DR: A semi-automated method for improving a cognitive model called Learning Factors Analysis is proposed that combines a statistical model, human expertise and a combinatorial search to evaluate an existing cognitive model and to generate and evaluate alternative models.
Abstract: A cognitive model is a set of production rules or skills encoded in intelligent tutors to model how students solve problems. It is usually generated by brainstorming and iterative refinement between subject experts, cognitive scientists and programmers. In this paper we propose a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search. We use this method to evaluate an existing cognitive model and to generate and evaluate alternative models. We present improved cognitive models and make suggestions for improving the intelligent tutor based on those models.
511 citations
Proceedings Article•
01 Jan 1982
422 citations
23 Jun 2008
TL;DR: A new method for instantiating Bayesian Knowledge Tracing is offered, using machine learning to make contextual estimations of the probability that a student has guessed or slipped, which allows for more accurate and reliable student modeling in ITSs that use knowledge tracing.
Abstract: Modeling students' knowledge is a fundamental part of intelligent tutoring systems. One of the most popular methods for estimating students' knowledge is Corbett and Anderson's [6] Bayesian Knowledge Tracing model. The model uses four parameters per skill, fit using student performance data, to relate performance to learning. Beck [1] showed that existing methods for determining these parameters are prone to the Identifiability Problem:the same performance data can be fit equally well by different parameters, with different implications on system behavior. Beck offered a solution based on Dirichlet Priors [1], but, we show this solution is vulnerable to a different problem, Model Degeneracy, where parameter values violate the model's conceptual meaning (such as a student being more likely to get a correct answer if he/she does not know a skill than if he/she does).We offer a new method for instantiating Bayesian Knowledge Tracing, using machine learning to make contextual estimations of the probability that a student has guessed or slipped. This method is no more prone to problems with Identifiability than Beck's solution, has less Model Degeneracy than competing approaches, and fits student performance data better than prior methods. Thus, it allows for more accurate and reliable student modeling in ITSs that use knowledge tracing.
402 citations
01 Jul 1988
TL;DR: A theory of Impasse-Driven Learning and socializing the Intelligent Tutor: Bringing Empathy to Computer Tutors are introduced.
Abstract: 1. The Computer as a Tool for Learning Through Reflection.- 2. Toward a Theory of Impasse-Driven Learning.- 3. Psychological Evaluation of Path Hypotheses in Cognitive Diagnosis.- 4. Modeling the Knowledge Base of Mathematics Learners: Situation-Specific and Situation-Nonspecific Knowledge.- 5. The Knowledge Engineer as Student: Metacognitive Bases for Asking Good Questions.- 6. Toward a Theory of Curriculum for Use in Designing Intelligent Instructional Systems.- 7. Enhancing Incremental Learning Processes with Knowledge-Based Systems.- 8. Mental Models and Metaphors: Implications for the Design of Adaptive User-System Interfaces.- 9. Improvement of the Acquisition of Knowledge by Informing Feedback.- 10. Socializing the Intelligent Tutor: Bringing Empathy to Computer Tutors.- 11. Cognitive Economy in Physics Reasoning: Implications for Designing Instructional Materials.- 12. Experimental Data for the Design of a Microworld-Based System for Algebra.- 13. Computer-Aided Model Building.
358 citations