Capturing and Building Expertise in Virtual Worlds

被引:0
|
作者
Freeman, Jared [1 ]
Stacy, Webb [1 ]
MacMillan, Jean [1 ]
Levchuk, Georgiy [1 ]
机构
[1] Aptima Inc, Washington, DC 20036 USA
关键词
Adaptive instruction; knowledge engineering; Constraint Logic Programming; Markov Decision Process;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Model-driven simulation can make the design and delivery of instruction more efficient and effective. We describe two computational models that support both the design and delivery of instruction. BEST (the Bench-marked Experiential System for Training) can guide experts through the space of domain problems during the knowledge engineering phase of instructional design; it can guide trainees through the space of training objectives during instruction. PRESTO (Pedagogically Relevant Engineering of Scenarios for Training Objectives) builds scenarios on the fly to elicit the knowledge of experts during instructional design, and to satisfy the instructional objectives of trainees.
引用
收藏
页码:148 / 154
页数:7
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