Developing a Statewide Professional Development Network for Effective Teaching of Undergraduate Quantitative Reasoning Course

该项目将通过改善旨在为本科生攻读STEM以外学位的介绍性定量扫盲课程的教学来服务于国家利益。量化扫盲可以改善与健康，财务和公民身份相关的决策，使人们能够做出明智的决定。该项目将为俄亥俄州的2年制和4年制机构的定量推理（QR）课程的教师提供专业发展。这些课程正在标准化过程中，保证学分可以在任何国家的公立高等教育机构之间转移。该项目旨在培养成对的数学教师，作为机构领导者，他们将在全州更大的QR教育工作者网络中支持他们的同事。 在3年的时间里，该网络计划跨越24个机构，吸引160多名教师。像许多其他州一样，俄亥俄州的数学教师队伍是多样化的;它包括兼职教师，每年签约的教师，研究生助教（GTA）和教双招生课程的高中教师。该项目希望解决如何最好地准备和支持广泛的教学队伍，以大规模教授高质量的QR。该项目的网络模式和调查结果可适用于其他网关课程（例如，大学代数，Precalculus，微积分，统计学和数据科学）由其他国家或多机构的合作伙伴关系。项目团队将通过实施和修订周期来处理项目的专业发展和研究。这种迭代过程旨在加强网络及其在项目生命周期中的机制。教师专业发展将侧重于探究式学习（IBL）的方法，以QR教学，把教学质量的前沿和中心。该网络将有助于俄亥俄州的QR途径与俄亥俄州高等教育部的全州学生转学和成功计划保持一致。课程内容和教学策略将与全州的QR要求保持一致，帮助参与教师将探究融入他们的教学中。教师将专注于设计和引入认知要求高的QR评估，并为学生提供参与高层次数学交流的机会。该项目的研究议程计划将重点放在教学质量的衡量IQA工具包，一个工具，最近才被应用于高等教育。课堂观察和学生工作的分析将产生新的知识，QR教师如何可能从事他们的学生在认知要求高的任务和高层次的数学交流。 项目评价将得到一名经验丰富的评价人员和一个高素质的咨询委员会的支持，该委员会将评估项目实现其目标的进展情况。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF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。

This project will serve the national interest by improving instruction in introductory quantitative literacy courses intended for undergraduates pursuing degrees outside of STEM. Quantitative literacy improves decision making related to health, finances, and citizenship, empowering people to make informed decisions. This project will provide professional development for instructors of Quantitative Reasoning (QR) courses at 2-year and 4-year institutions across Ohio. These courses are in the process of being standardized, guaranteeing credits can transfer between any of the state’s public institutions of higher education. The project intends to prepare pairs of mathematics instructors to serve as institutional leaders who will support their colleagues within a larger network of QR educators across the state. Over a 3-year period, the network plans to span 24 institutions and engage over 160 faculty. Like many other states, Ohio’s mathematics teaching workforce is diverse; it includes adjuncts, annually contracted faculty, graduate teaching assistants (GTAs), and high school teachers who teach dual-enrollment courses. The project hopes to address how best to prepare and support a broad instructional workforce to teach high-quality QR at scale. The project’s network model and findings could be adapted to additional gateway courses (e.g., College Algebra, Precalculus, Calculus, Statistics, and Data Science) by other states or multi-institutional partnerships.The project team will approach the project's professional development and research through a cycle of implementation and revision. This iterative process is meant to strengthen the network and its mechanisms over the life of the project. Faculty professional development will focus on an inquiry-based learning (IBL) approach to QR instruction, placing instructional quality front and center. The network will contribute to an Ohio QR pathway aligned with the Ohio Department of Higher Education’s statewide program for student transfer and success. Course content and instructional strategies will be aligned with statewide QR requirements, helping participating faculty weave inquiry into their teaching. Faculty will focus on designing and introducing cognitively demanding QR assessments and offering students the chance to engage in high-level mathematics communication. The project’s research agenda plans to focus on instructional quality as measured by the IQA toolkit, a tool that only recently have been applied to higher education. Classroom observations and analysis of student work will generate new knowledge on how QR instructors might engage their students in cognitively demanding tasks and high-level mathematical communication. The project evaluation will be supported by an experienced evaluator and a highly-qualified advisory board that will assess the project’s progress towards its goals. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.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.