Collaborative Research: Using Data Mining and Observation to derive an enhanced theory of SRL in Science learning environments

本项目旨在加强科学学习中学生自我调节学习过程的理论和测量。SRL指的是由元认知（思考自己的思维）、战略行动（根据标准计划、监控和评估个人进步）和学习动机指导的学习。该项目将通过开发一个基于技术的框架来实现这一目标，该框架利用人类专家判断和机器学习方法来识别SRL中的关键时刻，并对这些时刻进行深入分析。该项目使用现有的科学学习环境“贝蒂的大脑”，将建模学习与批判性思维和解决问题的技能结合起来，教授复杂的科学主题。该环境旨在让学生使用概念图（科学的关键要素）和环境提供的阅读材料来教授Betty科学主题。这个项目的一个关键部分是确定使用系统的学生何时需要帮助。以SRL为基础，添加到Betty的大脑将识别SRL过程中元认知、战略行动和动机的关键点。其中一些点可以在识别关键点时自动确定，而另一些点则需要人工干预来识别关键点，然后确定应该采取哪些行动来使学生能够学习。这些信息将被记录下来，然后这些经验将被用于更新关键点的自动识别。该项目的主要智力优势在于整合了数据挖掘的能力，可以快速筛选大量数据，找到学生推理和策略中的关键拐点或变化点，并结合了人类深入理解其他人的SRL过程的能力。这个测量框架，连同在线学习中学生SRL的拐点检测器，有可能通过提供关于SRL如何通过影响、参与、认知和元认知之间的相互作用在学习过程中展开的见解，改变K-12环境中的科学学习和教学。这些见解将用于扩展现有的SRL理论，增加其丰富性、特异性和预测能力。自我调节学习对学生的成功很重要，无论是在K-12教育中，还是在之后的终身学习中。更好地理解SRL过程将支持基于计算机的科学学习环境的发展，例如贝蒂的大脑，能够更好地支持学生在科学课堂上学习SRL技能和策略。通过在目前使用贝蒂大脑的不同城市学生群体中研究这些问题，该项目的成功将增加SRL与美国学习者多样性的相关性。该项目的软件将通过门户网站www.teachableagents.org提供。

This project aims to enhance theory and measurement of students' self-regulated learning (SRL) processes during science learning. SRL refers to learning that is guided by metacognition (thinking about one's thinking), strategic action (planning, monitoring, and evaluating personal progress against a standard), and motivation to learn. The project will accomplish this by developing a technology-based framework which leverages human expert judgment and machine learning methods to identify key moments during SRL and analyze these moments in depth. The project uses an existing science learning environment, Betty's Brain, that combines learning-by-modeling with critical thinking and problem solving skills to teach complex science topics. The environment is designed to have the student teach Betty science topics using concept maps (the critical elements of the science) and reading materials provided by the environment. A critical component of this project is to determine when a student using the system needs help. Using SRL as a basis, the additions to Betty's brain will identify key points in the SRL processes of metacognition, strategic action, and motivation. Some of these points can be determined automatically in recognizing key points while others require human intervention to recognize key points and then to determine what actions should be taken to enable the student. This information will be recorded and the experiences then are used to update the automatic identification of key points.The project's main intellectual merit is in integrating the power of data mining to rapidly sift through large amounts of data to find key inflection or change points in student reasoning and strategies, with the power of human beings to deeply understand other humans' SRL processes. This measurement framework, with the accompanying detectors of inflection points in students' SRL in online learning, has the potential to transform science learning and teaching in K-12 settings by providing insights into how SRL unfolds during learning through the interactions between affect, engagement, cognition and metacognition. Those insights will be used to extend an existing theory of SRL, increasing its richness, specificity, and predictive power. Self-regulated learning is important to student success, both in K-12 education and during life-long learning afterwards. Better understanding of SRL processes will support the development of computer-based science learning environments, such as Betty's Brain, with the capability to better support students' learning of SRL skills and strategies in science classrooms. By studying these issues within the diverse population of urban students who currently use Betty's Brain, success of the project will increase the relevance of SRL to the full diversity of America's learners. The project's software will be available through the portal www.teachableagents.org.