Developing a Scalable Measure of Inclusive STEM Teaching Practices for Diverse Institutions

最近，关于扩大STEM参与的心理学研究已经从以学生为中心的干预（例如，给予学生的归属感或成长心态干预）转向以情境为中心的干预（旨在让教师创造归属感或成长文化）。然而，评估由这些项目引起的教师实践变化的工具一直缺乏。流行的教师自我报告方法可能由于错误的回忆或社会可取性偏见而存在偏见，而更密集的观察方法则繁琐且难以维持。此外，现有的措施往往是在一种类型的机构内制定的，可能会低估传统黑人学院和大学以及西班牙裔服务机构的教师的包容性做法。这些限制减缓了发现可以扩大STEM参与的项目的进展。目前的提案旨在开发一个可扩展的、值得信赖的教师和学生报告系统，该系统可能成为评估下一代以环境为中心的干预措施的研究支柱。该项目将制定包容性高等教育STEM教学实践的新措施，为少数群体成员创造归属感和动力。将采用最佳调查设计来制定包容性实践措施，包括与教师和学生共同开发的半结构化访谈和认知预测试。这些措施将在三所大学同时制定和完善：一所以白人为主的大学，一所以西班牙裔为主的大学，以及一所历史上以黑人为主的大学。这种跨站点验证将确保测量对学生体验的可变性敏感，并增加其潜在的应用。这些措施在捕获实践频率和质量方面的敏感性将通过第三方观察、重复的学生报告调查和课程完成结果进行验证。机器学习算法，贝叶斯加性回归树（BART），将告知选择最佳数量的实践项目，以最大限度地减少受访者的负担，同时最大化预测。由此产生的措施将被广泛传播，通过让科学家和实践者了解什么是有效的以及为什么有效，从而释放他们制定包容性实践的创造潜力。本项目由美国国家科学基金会EHR核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。该项目支持积累有力的证据，为理解、构建理论进行解释提供依据，并提出干预和创新建议，以应对教育中持续存在的挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Recently, psychological research on broadening participation in STEM has shifted from student-focused interventions (e.g., belonging or growth mindset interventions given to students) to context-focused interventions (aimed at instructors to create cultures of belonging or growth). Tools for evaluating the changes in instructors’ practices induced by these programs have been lacking, however. Popular self-report methods for instructors can be biased due to faulty recall or social desirability bias, while more intensive observational methods are cumbersome and difficult to sustain. In addition, existing measures tend to be developed within one type of institution and may underemphasize inclusive practices from instructors at historically Black colleges and universities and Hispanic-serving institutions. These limitations have slowed progress toward the discovery of programs that could broaden participation in STEM. The present proposal seeks to develop a scalable, trustworthy system of instructor- and student-reports that could be the backbone for research evaluating the next generation of context-focused interventions. This project will develop novel measures of inclusive postsecondary STEM teaching practices that create an experience of belonging and motivation for members of minoritized groups. Measures of inclusive practices will be developed using optimal survey design, including semi-structured interviews and cognitive pretesting co-developed with both instructors and students. The measures will be simultaneously developed and refined across three universities: at a predominantly white-serving institution, a predominantly Hispanic-serving institution, and a historically-Black university. This cross-site validation will ensure that measures are sensitive to the variability in students’ experiences and increase their potential application. The sensitivity of these measures in capturing the frequency and quality of practices will be validated using third-party observations, repeated student-report surveys, and course completion outcomes. A machine-learning algorithm, Bayesian Additive Regression Trees (BART), will inform the choice of the optimal number of practices items that minimize respondent burden while maximizing prediction. The resulting measures will be widely disseminated to unlock the creative potential of scientists and practitioners developing inclusive practices by allowing them to learn what is working, and why.This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.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.