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

这是一个研究教学代理的奖项。教学代理是一种具体化的软件代理，它已经成为一种促进有效学习的有前途的工具。他们提供量身定制的解决问题的经验和建议，这些经验和建议是为特定环境下的个别学习者量身定制的。通过与学习者共同居住在一个丰富的基于探究的学习环境中，教学代理可以观察学习者解决问题的活动，提供适当的建议，并积极支持学习者通过提问、假设生成、数据收集和假设检验的循环迭代。然而，基于探究的学习也提出了一个重大挑战：学习环境的“开放性”为教学计划引入了多种复杂性来源。为了解决与支持基于探究的学习相关的复杂性，该项目提出使用贝叶斯教学代理，利用贝叶斯和决策理论推理计算模型的最新进展来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套完整的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影响决策的众多因素，从学习者解决问题的行动中推断出学习者的信念、目标和计划，包括策略的使用。通过将教学代理引入具有视觉吸引力的高端游戏平台环境中，并将其嵌入动态生成的科学叙述中，它将解决成就和吸引力的互补目标。该项目还将通过开展广泛的实证研究，全面介绍在基于探究的科学学习中与贝叶斯教学代理互动的认知过程和结果。为了了解中学生与贝叶斯教学代理互动时自主探究性科学学习发生的认知机制，该项目将采用多方法研究贝叶斯教学代理的使用和有效性。在受控的实验室和基于课堂的实地环境中，这些研究将探讨自我调节的核心问题，包括成就（科学内容知识、转移和有效的策略使用，包括策略选择和策略转移）和参与(自我效能感、情境兴趣、以及强调持久性的掌握导向，以精确地确定哪些技术和条件对学习过程和结果贡献最有效。贝叶斯网络与教育研究的结合是其智力上的优点。更广泛的影响在于教育技术的大幅改进。

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.