Pedagogical Agents to Support Embodied, Discovery-Based Learning
Pedagogical Agents to Support Embodied, Discovery-Based Learning
复制标题
支持实体化、基于发现的学习的教学代理
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Michael Neff
中科院分区：
文献类型：
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作者：
Ahsan Abdullah;Mohammad Adil;Leah F. Rosenbaum;M. Clemmons;Mansi Shah;Dor Abrahamson;Michael Neff
本文提出了一个教学代理，旨在支持学生在一个具体的，基于发现的学习环境。以发现为基础的学习引导学生通过一系列旨在培养特殊见解的活动。在这种情况下，动画代理解释如何使用比例数学图像训练器，提供性能反馈，引导学生获得不同的体验，并在需要时提供补救指导。对于智能体技术来说，这是一项具有挑战性的任务，因为来自学习者的具体反馈数量非常有限，这里仅限于屏幕上两个标记的位置。基于对教程协议的深入理解，动态决策网络用于自动确定代理行为。一项试点评估表明，所有参与者都制定了支持原始比例推理的运动方案。他们能够为其中一种策略提供语言原型比例表达，但不能为另一种策略提供。
This paper presents a pedagogical agent designed to support students in an embodied, discovery-based learning environment. Discovery-based learning guides students through a set of activities designed to foster particular insights. In this case, the animated agent explains how to use the Mathematical Imagery Trainer for Proportionality, provides performance feedback, leads students to have different experiences and provides remedial instruction when required. It is a challenging task for agent technology as the amount of concrete feedback from the learner is very limited, here restricted to the location of two markers on the screen. A Dynamic Decision Network is used to automatically determine agent behavior, based on a deep understanding of the tutorial protocol. A pilot evaluation showed that all participants developed movement schemes supporting proto-proportional reasoning. They were able to provide verbal proto-proportional expressions for one of the taught strategies, but not the other.