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

最近，关于扩大STEM参与度的心理学研究已经从以学生为中心的干预(例如，向学生提供归属感或成长心态干预)转向以情境为重点的干预(旨在指导教师创造归属感或成长文化)。然而，缺乏评估这些项目引起的教师实践变化的工具。流行的教师自我报告方法可能会因为错误的回忆或社会期望偏差而产生偏见，而更密集的观察方法则繁琐且难以维持。此外，现有措施往往是在一种类型的机构内制定的，可能会低估历史上黑人学院和大学以及为拉美裔服务的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参与度的项目的进展。目前的建议旨在开发一个可扩展的、值得信赖的教师和学生报告系统，作为评估下一代以背景为重点的干预措施的研究的支柱。该项目将制定包容各方的中学后STEM教学实践的新措施，为小规模群体的成员创造归属感和动力的体验。将使用最佳调查设计，包括与教师和学生共同开发的半结构访谈和认知前测，来制定包容性做法的衡量标准。这些措施将在三所大学同时制定和完善：一所主要为白人服务的大学，一所主要为西语裔服务的机构，以及一所历史上为黑人服务的大学。这种跨站点验证将确保措施对学生经验中的可变性敏感，并增加其潜在应用。将使用第三方观察、重复的学生报告调查和课程完成结果来验证这些措施在捕获练习频率和质量方面的敏感性。一种机器学习算法，贝叶斯加性回归树(BART)，将通知选择最优数量的练习项目，以最小化受访者的负担，同时最大化预测。由此产生的措施将被广泛传播，以释放科学家和实践者的创造性潜力，通过让他们了解什么是有效的，以及为什么。该项目得到了NSF的EHR核心研究(ECR)计划的支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。该计划支持积累强有力的证据，为理解、构建理论以解释并建议干预和创新以应对教育中持续存在的挑战的努力提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.