Topic
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.
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TL;DR: Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP.
Abstract: Cognitive psychology, artificial intelligence, and computer technology have advanced to the point where it is feasible to build computer systems that are as effective as intelligent human tutors Computer tutors based on a set of pedagogical principles derived from the ACT theory of cognition have been developed for teaching students to do proofs in geometry and to write computer programs in the language LISP
3,092 citations
Proceedings Article•
01 Jan 1999
TL;DR: An in-depth summary and analysis of the research and development state of the art for intelligent tutoring system (ITS) authoring systems and the major unknowns and bottlenecks to having widespread use of ITS authoring tools.
Abstract: This paper consists of an in-depth summary and analysis of the research and development state of the art for intelligent tutoring system (ITS) authoring systems. A seven-part categorization of two dozen authoring systems is given, followed by a characterization of the authoring tools and the types of ITSs that are built for each category. An overview of the knowledge acquisition and authoring techniques used in these systems is given. A characterization of the design tradeoffs involved in building an ITS authoring system is given. Next the pragmatic questions of real use, productivity findings, and evaluation are discussed. Finally, I summarize the major unknowns and bottlenecks to having widespread use of ITS authoring tools. (http://aied.inf.ed.ac.uk/members99/archive/vol_10/murray/full.html)
709 citations
TL;DR: Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition.
Abstract: AutoTutor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the .NET framework and has simplified deployment for classroom trials.
594 citations
TL;DR: In this paper, the role of affective states play in learning was investigated from the perspective of a constructivist learning framework, where six different affect states (frustration, boredom, flow, confusion, eureka and neutral) were observed during the process of learning introductory computer literacy with AutoTutor.
Abstract: The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent tutoring system with tutorial dialogue in natural language. Observational analyses revealed significant relationships between learning and the affective states of boredom, flow and confusion. The positive correlation between confusion and learning is consistent with a model that assumes that cognitive disequilibrium is one precursor to deep learning. The findings that learning correlates negatively with boredom and positively with flow are consistent with predictions from Csikszentmihalyi’s analysis of flow experiences.
589 citations
01 Jan 1993
TL;DR: This chapter discusses Tutoring Systems and Pedagogical Theory: Representational Tools for Understanding, Planning, and Reflection in Problem Solving, and the Role of the Tutor in Computer-Based Collaborative Learning Situations.
Abstract: Contents: S.J. Derry, S.P. Lajoie, A Middle Camp for (Un)Intelligent Instructional Computing: An Introduction. Part I:Model Builders. K.R. Koedinger, J.R. Anderson, Reifying Implicit Planning in Geometry: Guidelines for Model-Based Intelligent Tutoring System Design. V.J. Shute, A Comparison of Learning Environments: All That Glitters... M.R. Lepper, M. Woolverton, D.L. Mumme, J-L. Gurtner, Motivational Techniques of Expert Human Tutors: Lessons for the Design of Computer-Based Tutors. S.J. Derry, L.W. Hawkes, Local Cognitive Modeling of Problem-Solving Behavior: An Application of Fuzzy Theory. Part II:Non-Modelers. K. Reusser, Tutoring Systems and Pedagogical Theory: Representational Tools for Understanding, Planning, and Reflection in Problem Solving. G. Salomon, On the Nature of Pedagogic Computer Tools: The Case of the Writing Partner. R. Lehrer, Authors of Knowledge: Patterns of Hypermedia Design. S.D. Teasley, J. Roschelle, Constructing a Joint Problem Space: The Computer as a Tool for Sharing Knowledge. Part III:Bridging Differences in Opposing Camps. S.P. Lajoie, Computer Environments as Cognitive Tools for Enhancing Learning. S. Katz, A. Lesgold, The Role of the Tutor in Computer-Based Collaborative Learning Situations. L. Schauble, K. Raghavan, R. Glaser, The Discovery and Reflection Notation: A Graphical Trace for Supporting Self-Regulation in Computer-Based Laboratories. Part IV:Discussants. S.F. Chipman, Gazing Once More Into the Silicon Chip: Who's Revolutionary Now? A. Lesgold, Information Technology and the Future of Education.
589 citations