Bayesian Pedagogical Agents for Dynamic High-Performance Inquiry-Based Science Learning Environments

这是一个研究教学代理人的奖项。教学代理是具体化的软件代理，已成为促进有效学习的有前途的工具。它们提供定制的问题解决体验和建议，这些体验和建议恰好是为特定环境中的个别学习者量身定做的。通过与学习者共处一个丰富的探究式学习环境，教学代理可以观察学习者的问题解决活动，提供情景建议，并积极支持学习者在提问、假设生成、数据收集和假设检验的循环中迭代。然而，探究式学习也带来了巨大的挑战：学习环境的开放性给辅导计划带来了多个复杂性来源。为了解决与支持基于探究的学习相关的复杂性，该项目建议使用贝叶斯教学代理，利用贝叶斯推理和决策理论推理计算模型的最新进展，促进既有效又吸引人的自我调节学习体验。它将为基于探究的科学学习环境开发一整套贝叶斯教学代理技术。为了促进有效和引人入胜的学习过程和结果，它将创建贝叶斯教学代理，利用概率计算模型系统地推理与决策有关的多种因素，从他们解决问题的行动中推断学习者的信念、目标和计划，包括策略使用。通过将教学代理引入高端游戏平台典型的视觉吸引人的环境中，并将其嵌入动态生成的科学叙事中，它将解决成就和参与的互补目标。该项目还将通过开展广泛的实证研究，全面介绍在探究性科学学习中与贝叶斯教学主体互动的认知过程和结果。为了了解中学生与贝叶斯教学主体互动时自主探究式科学学习的认知机制，该项目将采用多种方法调查贝叶斯教学主体的使用和有效性。在受控实验室和基于课堂的实地环境中，这些研究将调查关于成就(科学内容知识、转移和有效策略使用，包括策略选择和策略转换)和参与度(自我效能、情景兴趣和掌握倾向，重点是坚持)的自我调节的中心问题，以准确确定哪些技术和条件对学习过程和结果最有效。其学术价值在于贝叶斯网络与教育研究的结合。更广泛的影响在于极大地改进了教育技术的前景。

This is an award to study pedagogical agents. Pedagogical agents are embodied software agents that have emerged as a promising vehicle for promoting effective learning. They provide customized problem-solving experiences and advice that are precisely tailored to individual learners in specific contexts. By co-habiting a rich inquiry-based learning environment with learners, pedagogical agents can observe learners' problem solving activities, offer situated advice, and actively support learners' iterating through cycles of questioning, hypothesis generation, data collection, and hypothesis testing. However, inquiry-based learning also presents a significant challenge: the very "openness" of the learning environment introduces multiple sources of complexity into tutorial planning. To address the complexities associated with supporting inquiry-based learning, this project proposes the use of Bayesian pedagogical agents that leverage recent advances in Bayesian and decision-theoretic computational models of reasoning to promote self-regulated learning experiences that are both effective and engaging. It will develop a full suite of Bayesian pedagogical agent technologies for inquiry-based science learning environments. To promote effective and engaging learning processes and outcomes, it will create Bayesian pedagogical agents that leverage probabilistic computational models that systematically reason about the multitude of factors that bear on decision making to infer learners' beliefs, goals, and plans, including strategy use, from their problem-solving actions. By introducing pedagogical agents into the visually engaging environments that typify high-end game platforms and embedding them in dynamically generated science narratives, it will address the complementary goals of achievement and engagement. The project will also provide a comprehensive account of the cognitive processes and results of interacting with Bayesian pedagogical agents in inquiry-based science learning by conducting extensive empirical studies. To understand the cognitive mechanisms by which self-regulated inquiry-based science learning occurs with middle school students interacting with Bayesian pedagogical agents, the project will take a multi-method approach to investigating the use and effectiveness of Bayesian pedagogical agents. In both controlled laboratory and classroom-based field settings, these studies will investigate the central issues of self-regulation with respect to both achievement (science content knowledge, transfer, and effective strategy use, including strategy selection and strategy shifting) and engagement (self-efficacy, situational interest, and mastery orientation with an emphasis on persistence) to determine precisely which technologies and conditions contribute most effectively to learning processes and outcomes. The intellectual merit lies in the marriage of Bayesian networks and education research. The broader impact is in the promise of vastly improved technology for education.