Evaluating intelligent tutoring with gaming-simulations
WSC '95 Proceedings of the 27th conference on Winter simulation
A generic architecture for intelligent instruction for simulation modelling software packages
WSC '96 Proceedings of the 28th conference on Winter simulation
A generic architecture for intelligent simulation training systems
ANSS '91 Proceedings of the 24th annual symposium on Simulation
Modeling reality with simulation games for a cooperative learning
Proceedings of the 32nd conference on Winter simulation
The Web and model-centered instruction
Web-based Training
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Intelligent Guide: Combining User Knowledge Assessment with Pedagogical Guidance
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
KM QUEST: a collaborative internet-based simulation game
Simulation and Gaming - Special issue: Simulation & gaming
KM QUEST: a collaborative internet-based simulation game
Simulation and Gaming - Special issue: Simulation & gaming
Using Explanations of Agents to Increase Understanding of Simulations for Tutoring Police Allocation
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Analyzing police patrol routes by simulating the physical reorganization of agents
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
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This article describes the ExpertCop tutorial system, a simulator of the crime in an urban region. In ExpertCop, the students (police officers) configure and allocate an available police force according to a selected geographic region and then interact with the simulation. The student interprets the results with the help of an intelligent tutor, the Pedagogical Agent, observing how the crime behaves in the presence of the allocated preventive policing. The interaction between domain agents representing social entities as criminals and police teams drives the simulation. ExpertCop induces students to reflect on resource allocation. The pedagogical agent implents interaction strategies between the student and the geosimulator, designed to make simulated phenomena better understood. In particular, the agent uses a machine learning algorithm to identify patterns on simulation data and to formulate questions to the student about these patterns. Moreover, it explores the reasoning process of the domain agents by providing explanations that help the student to understand simulation events.