Foundations of Intelligent Tutoring Systems

Polson, Martha C.; Richardson, J

doi:10.4324/9780203761557

Cited by 143 publications

(35 citation statements)

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“…Representing the knowledge in a declarative form, as in an ontology, reduces the coupling of the knowledge representation and the system that uses it (domain of knowledge engineering). In this way, coupling MAS with ontology seems to be appropriate as it lets to the agents the actions to perform but also the possibility to reason with the knowledge in order to solve problems at both a local (related to a single agent) and a global (related to a group of agents) level (Polson and Richardson, 2013). Four agent models of generalization are reviewed and discussed in this section.…”

Section: Multi-agent System (Mas) and Generalizationmentioning

confidence: 99%

An ontology-driven multi-agent system for nautical chart generalization

Yan

Guilbert

Saux

2016

Cartography and Geographic Information Science

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On nautical charts, undersea features are portrayed by sets of soundings (depth points) and isobaths (depth contours) from which map readers can interpret undersea features. Different techniques were developed for automatic sounding selection and isobath generalization. These methods are mainly used to generate a new chart from the bathymetric database or from a larger scale chart through selection and simplification. However a part of the process consists in selecting and emphasizing undersea features formed by groups of soundings and isobaths on the chart according to their relevance to maritime navigation. Hence automation of the process requires classification of features and their generalization through the application of a set of operators according not only to geometric constraints but also to their meaning.The objective of this work is to propose a multi-agent system for nautical chart generalization that is driven by the knowledge on the generalization process and the undersea features and their relationships. Firstly, this work provides a featurecentered ontology modeling the generalization process. Then, the MAS structure is introduced where agents access cartographic knowledge stored in the ontology. The MAS makes use of measure algorithms to evaluate constraint violations on the chart in order to decide which generalization operators to apply. The whole model has been implemented to provide generalization plans on a real case study.

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Section: Multi-agent System (Mas) and Generalizationmentioning

confidence: 99%

An ontology-driven multi-agent system for nautical chart generalization

Yan

Guilbert

Saux

2016

Cartography and Geographic Information Science

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“…Refening to these recommendations, one important issue for knowledge development concerns the beneficial effects of stand-alone instructional games provided with intelligent tutoring functions (Polson & Richardson, 1988), which are needed to compensate for the lack of human instructional support. Studying this issue will provide more insight into the potential of stand-alone instructional games and (required) supporting components.…”

Section: Transfer Of Gaming 1321 Learning Approachmentioning

confidence: 99%

Transfer of Training of An Educational Serious Game: The Effectiveness of the CASHIER TRAINER

Oprins¹,

Korteling²

2013

Gaming Media and Social Effects

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In this study transfer of gaming is investigated for a particular case, the Cashier Trainer. This is an instructional simulation game with an intelligent tutoring system that can be done individually. Its purpose is to train new cashier employees. (Sitzmann, 201l). This is especially useful when conventional training, e.g. on-the-job training (OJT) does not provide suitable opportunities because ofrisks caused by errors, costs ofusing the 'real' system, legislation, sustainability, training scenario generation, or the limited availability of the operational environment . In OJT a supervisor usually coaches the learner while performing tasks in the real-life set-

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“…The characteristics of an Intelligent Tutoring System include the ability to teach a given subject, and to detect student's errors, and to assist the student correct her mistakes 10,11 . Advancements in software over the last decade along with interactive multimedia development tools have created software environments that make real Intelligent Tutoring Systems a possibility 12,13,14 . Recently 15,16,17 promoted intelligent agents and systems with intelligence have been proposed for a multitude of tasks [18][19][20] .…”

Section: Virtual Tutoring Environmentmentioning

confidence: 99%

An Intelligent Tutoring System for Multimedia Virtual Power Laboratory

Gong

Biswas²,

Bai³

et al.

2016 ASEE Annual Conference &Amp; Exposition Proceedings

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Saroj Biswas is a Professor of Electrical and Computer Engineering at Temple University specializing in electrical machines and power systems, multimedia tutoring, and control and optimization of dynamic systems. He has been the principle investigator of a project for the development of an intelligent tutoring shell that allows instructors create their own web-based tutoring system. His current research focuses on security of cyber-physical systems based on multiagent framework with applications to the power grid, and the integration of an intelligent virtual laboratory environment in curriculum. He is an associate editor of Dynamics of Continuous, Discrete and Impulsive Systems: Series B, and is a member of IEEE, ASEE, and Sigma Xi. INTELLIGENT TUTORING SYSTEM FOR MULTIMEDIA VIRTUAL LABORATORY IN POWER ENGINEERINGAbstract A laboratory practicum is considered a key element in traditional undergraduate education in power engineering, however often ignored for various reasons, such as expenses for establishing a physical laboratory, safety of students working with high voltage sources, and lack of qualified teaching assistants. This research is a continuation of an NSF funded project on the development of a Virtual Power Laboratory 1 (VPL) environment that can simulate an Electric Machines Laboratory. Development of the VPL architecture and its modules has been reported in previous years. This paper describes the final stage of the VPL architecture which is instructional design and implementation of an Intelligent Tutoring System. The virtual laboratory is supervised by a virtual Intelligent Tutor that can track students' progress and monitor their actions in the virtual laboratory platform. The VPL offers a virtual experimental environment with 2D graphics, 3D animations, audio guidance, simulations, knowledge concept bases, and virtual experiments; the Intelligent Tutoring System is designed on top of these functionalities. It processes the user actions and the resources existing in the virtual laboratory, and tracks students' progress, answers questions, monitors their actions, and if necessary, guides the students with prerequisite material on the subject matter.

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Foundations of Intelligent Tutoring Systems

Cited by 143 publications

References 0 publications

An ontology-driven multi-agent system for nautical chart generalization

An ontology-driven multi-agent system for nautical chart generalization

Transfer of Training of An Educational Serious Game: The Effectiveness of the CASHIER TRAINER

An Intelligent Tutoring System for Multimedia Virtual Power Laboratory

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