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CAREER: Multimedia Explanation Generation for Knowledge-Based Learning Environments

CAREER: Multimedia Explanation Generation for Knowledge-Based Learning Environments
职业:基于知识的学习环境的多媒体解释生成
批准号:
9701503
负责人:
James Lester
金额:
$37.33万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1997
资助国家:
美国
项目状态:
已结题
起止时间:
1997-06-01 至 2001-05-31

项目摘要

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中文摘要
翻译
基于知识的学习环境可以提供高度定制的问题解决体验,这些体验是根据每个学生的个人需求量身定做的。该项目通过开发带有动画教学代理的实时多媒体解释生成器,为新一代基于知识的学习环境创造了技术。在学习环境中,动画教学代理可以观察学生的进步,为他们提供可视化的解决问题的建议,并发挥强大的激励作用。该项目有三个主要任务:(1)开发一个实时多媒体解释规划的计算模型,用于动态创建定制的多媒体解释;(2)开发一个动画教学代理的计算模型;(3)对这些模型的教学效果进行实证评估。此外,该项目还开发了一个多媒体技术教学实验室,并开设了一门关于基于知识的多媒体学习环境的新的多学科课程。这项工作极大地推进了基于知识的学习环境和人机交互的最新水平。再加上负担得起的多媒体硬件的快速发展,这些技术的成功部署将对各个级别的学生产生深刻和持久的影响。通过在学习效率方面取得显著进展,这些技术将在广泛的课堂和工作场所带来根本性的改善。
英文摘要
Knowledge-based learning environments can provide highly customized problem-solving experiences that are tailored to the individual needs of each student. This project creates the technology for a new generation of knowledge-based learning environments by developing real-time multimedia explanation generators with animated pedagogical agents. Prominently featured in learning environments, animated pedagogical agents can observe students' progress, provide them with visually contextualized problem-solving advice, and play a powerful motivational role. The project has three major thrusts: (1) developing a computational model of real-time multimedia explanation planning for dynamically creating customized multimedia explanations; (2) developing a computational model of animated pedagogical agents; and (3) conducting empirical evaluations of the pedagogical effectiveness of these models. In addition, the project also develops a teaching laboratory for multimedia technology and initiates a new multidisciplinary course on Knowledge-Based Multimedia Learning Environments. This work significantly advances the state-of-the-art in knowledge- based learning environments and human-computer interaction. Coupled with rapid advances in affordable multimedia hardware, the successful deployment of these technologies will have a deep and lasting impact on students at all levels. By achieving significant gains in learning effectiveness, these technologies will bring about fundamental improvements in both the classroom and the workplace on a broad scale.
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ExplainIt: Improving Student Learning with Explanation-based Classroom Response Systems
  • 批准号:
    2111473
  • 项目类别:
    Standard Grant
  • 资助金额:
    $145.65万
  • 财政年份:
    2021
  • 负责人:
    James Lester
  • 依托单位:
AI Institute for Engaged Learning
  • 批准号:
    2112635
  • 项目类别:
    Cooperative Agreement
  • 资助金额:
    $1999.63万
  • 财政年份:
    2021
  • 负责人:
    James Lester
  • 依托单位:
Collaborative Research: PrimaryAI: Integrating Artificial Intelligence into Upper Elementary Science with Immersive Problem-Based Learning
  • 批准号:
    1934153
  • 项目类别:
    Standard Grant
  • 资助金额:
    $98.56万
  • 财政年份:
    2019
  • 负责人:
    James Lester
  • 依托单位:
EAGER: Collaborative Research: Building Capacity for K-12 Artificial Intelligence Education Research
  • 批准号:
    1938778
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2019
  • 负责人:
    James Lester
  • 依托单位:
海外基金