An Explanatory Machine Learning Framework for Teacher Effectiveness in STEM Education

STEM 教育中教师效能的解释性机器学习框架

• 批准号: 2321191
• 负责人: Benjamin Kelcey
• 金额: $ 35万
• 依托单位: University of Cincinnati Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2321191&HistoricalAwards=false
• 关键词: Explanatory; Machine; Learning; Framework; Teacher

This project aims to serve the national interest by developing explanatory machine learning methods for the study of teaching effectiveness in STEM education. There is consistent evidence that teachers vary widely in their effectiveness but conventional analytic methods have largely failed to explain why and under what contexts teaching and teachers vary. Explanatory machine learning methods hold significant potential in developing fundamental knowledge and theories of equitable and effective STEM teaching because they can track complex features, processes and patterns inherent in and implied by theories of teaching in ways where conventional methods fall short. In this project, we examine the extent to which we can leverage machine learning methods to identify and explain profiles, pathways and practices (e.g., who teachers are, what teachers know, what teachers believe, perceive and experience, what teachers do) that produce student learning and how these profiles and practices vary across STEM education contexts. The outcomes of this project have the potential to accelerate research on the theory and practice of effective teaching, teacher preparation, teacher development and student learning. This is a three-year BCSER: Individual Investigator Development project in STEM Education Research within Research on STEM Learning and Learning Environments.The fields of STEM education and teacher development have made substantial progress in developing sophisticated theories of teaching and learning and instruments and measures that support and operationalize research on those theories (e.g., teacher knowledge, culturally responsive teacher self-efficacy, classroom observations). Recent literature has, however, noted that there is a mismatch between the complexity found in our theories of effective teachers and teaching and the prevailing methods we use to analyze those theories. For example, theories suggest that teaching is a highly interactive, adaptive, nonlinear and context-dependent practice; yet the field has almost exclusively drawn on simple linear regression models that cannot readily detect and analyze these complex patterns. There is a growing recognition of the need to craft, develop and grow methodologies specific to the purposes of STEM teaching and learning research. This project aims to fill this gap by developing and adapting explanatory machine learning methods (e.g., neural networks) to analyze studies of teaching effectiveness and examining the extent to which these methods can predict, explain and contextualize effective teaching in ways that outperform conventional methods. The results have the potential to broadly build capacity and impact the field by identifying complex features and profiles of effective teaching within and across contexts and developing scalable machine learning methods that are broadly applicable to STEM education studies. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This project is also supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research in the core areas of STEM learning and learning environments, broadening participation in STEM fields, and STEM workforce development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过开发解释性机器学习方法来研究 STEM 教育的教学效果，从而服务于国家利益。有一致的证据表明，教师的有效性差异很大，但传统的分析方法在很大程度上无法解释教学和教师为何以及在何种背景下会发生差异。解释性机器学习方法在发展公平有效的 STEM 教学的基础知识和理论方面具有巨大潜力，因为它们可以以传统方法无法达到的方式跟踪教学理论固有的和隐含的复杂特征、过程和模式。在这个项目中，我们研究了我们可以在多大程度上利用机器学习方法来识别和解释档案、路径和实践（例如，教师是谁，教师知道什么，教师相信、感知和经验什么，教师做什么），从而产生学生的学习情况以及这些概况和实践在 STEM 教育背景下有何不同。该项目的成果有可能加速有效教学、教师准备、教师发展和学生学习的理论和实践研究。这是一个为期三年的 BCSER：STEM 学习和学习环境研究中 STEM 教育研究的个人研究者发展项目。STEM 教育和教师发展领域在开发复杂的教学理论以及工具和措施方面取得了实质性进展，支持和实施对这些理论的研究（例如教师知识、文化响应式教师自我效能、课堂观察）。然而，最近的文献指出，我们有效教师和教学理论的复杂性与我们用来分析这些理论的流行方法之间存在不匹配。例如，理论认为教学是一种高度互动、适应性、非线性和情境依赖的实践；然而，该领域几乎完全采用简单的线性回归模型，这些模型无法轻松检测和分析这些复杂的模式。人们越来越认识到需要精心设计、开发和发展专门针对 STEM 教学和学习研究目的的方法。该项目旨在通过开发和调整解释性机器学习方法（例如神经网络）来分析教学有效性研究并检查这些方法能够以优于传统方法的方式预测、解释和情境化有效教学的程度，从而填补这一空白。研究结果有可能通过识别上下文内和跨上下文有效教学的复杂特征和概况，并开发广泛适用于 STEM 教育研究的可扩展机器学习方法，广泛建设能力并影响该领域。该项目得到了比尔及梅琳达·盖茨基金会、施密特期货公司和沃尔顿家族基金会的合作支持。 该项目还得到了 NSF 的 EDU STEM 教育研究核心研究能力建设（ECR：BCSER）计划的支持，该计划旨在培养研究人员在 STEM 学习和学习环境的核心领域开展高质量 STEM 教育研究的能力、扩大 STEM 领域的参与以及 STEM 劳动力发展。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。