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Intelligent tutoring system

About: Intelligent tutoring system is a research topic. Over the lifetime, 3472 publications have been published within this topic receiving 58217 citations. The topic is also known as: ITS.


Papers
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20 Nov 1995
TL;DR: This thesis describes a model of tutoring intended for CIRCSIM-Tutor that teaches the functioning of the baroreceptor reflex to the first year medical students and uses multiple qualitative models of the domain in the process of facilitating knowledge integration.
Abstract: This thesis describes a model of tutoring. This model is intended for CIRCSIM-Tutor (v.3)--an Intelligent Tutoring System (ITS)--that teaches the functioning of the baroreceptor reflex to the first year medical students. This model is based on the behavior of human tutors in the keyboard-to-keyboard sessions. The major theme of this model is that, in a problem-solving environment, it helps the student integrate his/her knowledge into a coherent qualitative causal model of the domain and solve problems in the domain. The key feature of this model is that it uses multiple qualitative models of the domain in the process of facilitating knowledge integration. The development of this model of tutoring has been approached by using an ITS development framework that views the development of an ITS as a modeling activity. There are three major phases of this methodology. These are the conceptual phase, the system phase, and physical phase. At each phase a different model of an ITS results. The conceptual model, resulting out of the conceptual phase, deals in this research only with the domain and the pedagogy aspects of tutoring. The domain knowledge here consists of multiple qualitative models that are used to support decision making. This decision making process considers three major functions: what to teach, when to teach, and how to teach. The system model, resulting out of the system phase, provides a generic framework to represent three different types of knowledge. These are the planning knowledge, the curriculum knowledge, and the domain knowledge. The system model can also be viewed as consisting of a set of tutoring spaces. Each space is responsible for performing one type of major decision of the tutor while interacting with the student. The second model resulting out of the system phase is the architecture of the system. Here an object-oriented methodology is used to develop some of the major components of this architecture. These architectural components are coded using the Common Lisp Object System on the Apple Macintosh.

18 citations

Book ChapterDOI
22 Aug 2004
TL;DR: A multi-agent intelligent tutoring system building tool that integrates different formalisms in order to facilitate the teacher task of developing the contents of a tutorial system and at the same time to provide adaptiveness and flexibility in the presentation is proposed.
Abstract: In this paper we propose a multi-agent intelligent tutoring system building tool that integrates different formalisms in order to facilitate the teacher task of developing the contents of a tutorial system and at the same time to provide adaptiveness and flexibility in the presentation. The adopted formalisms are ground logic terms for the student model, data-bases for the domain model and object Petri nets for the pedagogical model. The interaction between the student and each agent of the system is controlled by an object Petri net, automatically translated into a rule-based expert system. The object Petri net tokens are composed by data objects that contain pointers to the student model and to the domain knowledge, stored into a data-base of texts, examples and exercises. The object Petri net transitions are controlled by logical conditions that refer to the student model and the firing of these transitions produce actions that update this student model.

18 citations

Proceedings Article
01 Jan 2011
TL;DR: This paper investigates ensembling at the post-test level, to see if this approach can produce better prediction of post- test scores within the context of a Cognitive Tutor for Genetics, and finds no improvement for ensembled over the best individual models.
Abstract: Over the last few decades, there have been a rich variety of approaches towards modeling student knowledge and skill within interactive learning environments. There have recently been several empirical comparisons as to which types of student models are better at predicting future performance, both within and outside of the interactive learning environment. A recent paper (Baker et al., in press) considers whether ensembling can produce better prediction than individual models, when ensembling is performed at the level of predictions of performance within the tutor. However, better performance was not achieved for predicting the post-test. In this paper, we investigate ensembling at the post-test level, to see if this approach can produce better prediction of post-test scores within the context of a Cognitive Tutor for Genetics. We find no improvement for ensembling over the best individual models and we consider possible explanations for this finding, including the limited size of the data set.

18 citations

Journal Article
TL;DR: The object of this study is to model the level of a question difficulty by a differential equation at a pre-specified domain knowledge, to be used in an educational support system.
Abstract: The object of this study is to model the level of a question difficulty by a differential equation at a pre-specified domain knowledge, to be used in an educational support system. For this purpose, we have developed an intelligent tutoring system for mathematics education. Intelligent Tutoring Systems are computer systems designed for improvement of learning and teaching processes in the domain knowledge. The developed system, which is called as MathITS, is based on conceptual map modeling.The Mathematica Kernel is used as an expert system and knowledge representation is based on LaTeX notation in MathITS.

18 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202322
202244
202199
2020110
2019138
2018165