CyberTraining:CIU:Computational and Data Science Literacy Curriculum Exchange

国家普遍需要一支具备适当计算和数据科学(CDS)知识的研究队伍。尽管在将CDS能力纳入大学课程方面取得了一些进展，但变化的步伐一直很慢。系的界限、STEM专业的课程要求和资源限制都是跨STEM学科CDS课程实施缓慢的原因。俄亥俄州超级计算机中心(OSC)与白求恩·库克曼大学(BCU)、克拉克·亚特兰大大学(CAU)、摩根州立大学(Morgan)、东南大学研究协会(SURA)、南方大学和农工学院(SUBR)以及波多黎各大学马亚圭兹分校(UPRM)合作，正在实施一项计算和数据科学(CDS)课程交换(C2Exchange)试点项目，以应对资源有限的机构所面临的挑战，尤其是少数族裔服务机构(MSI)所面临的挑战。正如NSF的使命所述，该项目服务于国家利益：通过推进STEM教育并提高我们未来研究人员的计算和数据科学技能，促进科学进步，服务于国家健康、繁荣和福利。课程交换方法旨在最大限度地减少提供新课程和更新课程所需的教师准备时间，增加每个机构提供的CDS课程的数量，并通过吸引所有参与机构的学生来提供最初可能在个别机构没有大量注册人数的课程。这项试点为CDS课程交换奠定了基础，该交换可以增加资源不足的小型MSI的能力，以低投资提供本科生CDS课程、辅修课程和证书，并可扩展以供更多资源受限的机构未来参与。这些机构的学生将受益于有机会获得更广泛的计算科学课程选择。对这些备受追捧的技能的培训旨在增加他们成为STEM劳动力的一部分或继续读研究生并成为有效研究人员的机会。每个学术机构都在提供和接收课程，如建模与仿真导论、计算化学以及数据可视化，目标是提供一系列CDS课程，这些课程可以成为每个机构证书或辅修课程的一部分。该联盟通过混合式在线学习模式提供课程，这种模式已经过测试，并被证明对技术科目有效。这种模式足够灵活，可以减少当地教师的准备时间，回应学生在材料上的困难，并适应机构的不同时间表。如果成功，这种交流教学模式将推广到广泛的机构以及研究生一级的专业教学。该项目探索了与在资源有限的环境中实施共享课程网络相关的机构和教学问题的重要见解。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。

There is a well-established national need for a research workforce with suitable knowledge of computational and data science (CDS). Although, some strides have been made in integrating CDS competencies into the university curriculum, the pace of change has been slow. Department boundaries, course requirements for STEM majors, and resource constraints all contribute to the slow pace of CDS curriculum implementation across STEM disciplines. Ohio Supercomputer Center (OSC) in collaboration with Bethune Cookman University (BCU), Clark Atlanta University (CAU), Morgan State University (Morgan), Southeastern Universities Research Association (SURA), Southern University and A&M College (SUBR), and the University of Puerto Rico at Mayaguez (UPRM) are implementing a pilot Computational and Data Science (CDS) Curriculum Exchange (C2Exchange) to address the challenges experienced by resource constrained institutions and, in particular, those faced by Minority Serving Institutions (MSIs). This project serves the national interest, as stated by NSF's mission: to promote the progress of science and to serve the national health, prosperity, and welfare by advancing STEM education and improving the computational and data science skills of our future research workforce. The curriculum exchange approach is intended to minimize the faculty preparation time required to deliver new and updated courses, increase the number of CDS courses offered at each institution, and allow courses to be offered that may not initially have large enrollments at an individual institution by drawing students from all of the participating institutions. This pilot develops the foundation for a CDS Curriculum Exchange that can increase the capacity of small under-resourced MSIs to deliver undergraduate CDS curriculum, minors, and certificates with low investment and be extensible for future participation by additional resource constrained institutions. Students in these institutions will benefit by having access to a broader selection of computational science courses. Training in these highly sought after skills aims to increase their chances of being part of the STEM workforce or continuing on to graduate school and becoming effective researchers. Each academic institution is contributing and receiving courses, such as Introduction to Modeling and Simulation, Computational Chemistry, as well as Data Visualization, with the goal of providing a sequence of CDS courses that can form part of a certificate or minor program at each institution. The consortium delivers courses through a blended online learning model that has been tested and shown to be effective for technical subjects. This model is flexible enough to reduce preparation time for local instructors, respond to students' difficulties with material, and to accommodate institutions' varied schedules. If successful, this exchange model of instruction will be extensible to a wide range of institutions as well as to specialized instruction at the graduate level. The project explores important insights into both the institutional and pedagogical questions associated with the implementation of shared course networks in resource constrained environments.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.