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教育中的教学效率。有一致的证据表明，教师的有效性差异很大，但是传统的分析方法在很大程度上未能解释为什么以及在哪些背景下教学和教师的不同。解释性的机器学习方法在开发公平和有效的STEM教学的基本知识和理论方面具有巨大的潜力，因为它们可以跟踪复杂的特征，过程和模式在教学理论中固有的，并以传统方法不足的方式跟踪。在这个项目中，我们研究了我们可以利用机器学习方法来识别和解释概况，路径和实践（例如，老师是谁，老师知道的，老师的信仰，感知和经验，老师的工作）以及这些概况以及这些个人资料和实践如何在STEM教育环境中变化。该项目的结果有可能加快对有效教学，教师准备，教师发展和学生学习的理论和实践的研究。这是一项为期三年的BCSER：STEM教育研究中的STEM教育研究中的个人研究者发展项目。STEM教育和教师发展领域在开发有关教学，学习和工具和工具和措施的复杂理论方面取得了重大进展，这些理论支持这些理论的研究和运营研究（例如，教师知识，教师知识，教师自我效能学教师自我效能学，课堂观察，课堂观察）。然而，最近的文献指出，在我们有效的教师理论和教学理论中发现的复杂性与我们用来分析这些理论的普遍方法之间存在不匹配。例如，理论表明教学是一种高度互动，适应性，非线性和上下文依赖性实践。然而，该领域几乎只借鉴了简单的线性回归模型，这些模型无法轻易检测和分析这些复杂模式。人们越来越认识到需要针对STEM教学研究目的而制造，发展和发展方法论的必要性。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）来分析教学效果的研究并检查这些方法可以在多大程度上以优于常规方法来预测，解释和上下文化有效的教学的程度。结果有可能通过确定在环境内和环境中的有效教学以及开发可扩展的机器学习方法，这些方法广泛地适用于STEM教育研究。该项目通过与Bill＆Melinda Gates Foundation，Schmidt Futures和Walton Family Foundation的合作伙伴关系得到支持。 该项目还得到了NSF的STEM教育研究（ECR：BCSER）计划中的EDU核心研究建设能力，该计划旨在建立研究人员在STEM学习和学习环境的核心领域进行高质量的STEM教育研究的能力，从而扩大了STEM领域的参与，并扩大了STEM领域的参与，并扩大了STEM劳动力的发展。审查标准。