Non-Profit and HBCU Collaboration to Establish a Plant Phenotyping Data Science Course-Based Undergraduate Research Experience

该项目旨在通过为一所历史悠久的黑人大学的本科生提供机会，通过基于课程的本科生研究经验(CURE)与植物和数据科学研究人员合作，增加STEM管道中的学生多样性，从而服务于国家利益。一条充满活力和多样化的STEM管道对国家经济、创新能力和经济繁荣至关重要。在美国，这一STEM管道依靠不同的背景、观点和经验来改进创新和成果，但像非裔美国人这样的少数族裔群体在管道中的参与率仍然低于他们在整个人口中的代表性。在STEM中建立和保持多样化和强大的管道的建议呼吁增加历史上黑人学院和大学(HBCU)的教师和传统白人学术机构的科学家之间的互利合作数量。这种合作的扩大将使HBCU本科生可以获得的真实研究经验的数量和类型有所增加。目前改善本科生STEM教育的努力包括采用能够成功地让大量本科生参与真正的科学研究的疗法。包括生物、生物化学和化学在内的不同学科的治疗方法正在本科院校中日益突出，成为扩大STEM渠道的成功战略，但它们在HBCU中并不突出。通过这个项目，唐纳德·丹福斯植物科学中心(DDPSC)(一个非学术、非营利性的植物科学研究机构)和哈里斯-斯托州立大学(HSSU)(HBCU)正在合作开发和提供植物和数据科学疗法。总体目标是增加STEM管道中的小规模学生数量。美国科学促进会2011年S的报告《愿景与变革》建议生物教育界优先通过积极参与研究来为学生提供实践科学的机会，而不仅仅是阅读。众所周知，本科生的研究经验对学业成绩、未来的职业兴趣和提高自我效能感有很大贡献。尽管有这样的好处，但从历史上看，这些研究经验并不容易为少数族裔服务机构的整个教室的学生提供。该项目将通过使用当前科学研究中的图像数据集，培训学生在图像分析和数据科学技术方面的尖端技能，提高未被充分代表的种族主义少数族裔学生的科研技能。我们将按专业和课程(生物学和数学)分析学生的各种学习结果，包括自我效能感、对数据科学的态度、知识获取曲线上的位置以及实验室课程评估调查(LCAS)的分数。我们将在我们的模型中包括可能影响知识获取和自我效能的变量，使用广义线性模型(GLM)，该模型包括态度、性别认同、种族/民族、教育背景、专业以及协作、发现/研究和迭代的分数。为了确定与数据科学家的合作如何影响学生对他们在研究中角色的感知，我们将使用混合方法来分析LCAS结果，这将量化学生对他们参与协作、发现/研究和迭代的感知。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生的STEM教育的有效性。通过参与的学生学习路径，该计划支持有前景的实践和工具的创建、探索和实施。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。

This project aims to serve the national interest by increasing the diversity of students in the STEM pipeline through the provision of opportunities for undergraduates at a historically black university to collaborate with plant and data science researchers via a course-based undergraduate research experience (CURE). A vibrant and diverse STEM pipeline is critical to the nation’s economy, innovation capability, and economic prosperity. In the United States, this STEM pipeline relies on diverse backgrounds, viewpoints, and experiences to improve innovation and outcomes, yet the participation of minority ethnic groups like African Americans in the pipeline remains lower than their representation within the population as a whole. Recommendations for building and sustaining a diverse and robust pipeline in STEM call for increasing the number of mutually beneficial collaborations between faculty at historically black colleges and universities (HBCUs) and scientists in traditionally white academic institutions. Expansion of such collaborations would enable the numbers and types of authentic research experiences available to undergraduate students at HBCUs to increase. Current efforts to improve undergraduate STEM education include the adoption of CUREs that can successfully engage large numbers of undergraduate students in authentic scientific research. CUREs in diverse disciplines including biology, biochemistry and chemistry are gaining prominence in undergraduate institutions as a successful strategy for growing the STEM pipeline, but they are not prominent at HBCUs. Through this project, the Donald Danforth Plant Science Center (DDPSC), a non-academic, non-profit plant science research facility, and Harris-Stowe State University (HSSU), a HBCU, are collaborating to develop and offer a plant and data science CURE. The overall goal is to increase the number of minoritized students in the STEM pipeline.The American Association for the Advancement of Science (AAAS)’s 2011 report, Vision and Change, recommended that the biology education community prioritize providing opportunities for students to practice science through active engagement in research rather than just reading about it. Experiences in research for undergraduate students are known to greatly contribute to academic achievement, future career interests, and increased self-efficacy. Despite such benefits, these research experiences have historically not been readily available to entire classrooms of students at minority-serving institutions. This project will enhance the scientific research skills of underrepresented racialized minority students through a CURE which will train student in cutting-edge skills in image analysis and data science techniques, using image datasets from current scientific research studies. By major and by course (biology vs math) we will analyze various student learning outcomes, including self-efficacy, attitudes towards data science, position on a knowledge acquisition curve, and Laboratory Course Assessment Survey (LCAS) scores. We will include in our model variables that might impact knowledge acquisition and self-efficacy using a generalized linear model (GLM) that includes attitude, gender identity, race/ethnicity, educational background, major, and scores for collaboration, discovery/research, and iteration. To determine how collaborating with data scientists impacts students’ perceptions of their role in research, we will use a mixed methods approach of analyzing the LCAS results which will quantify student’s perceptions of their engagement in collaboration, discovery/research, and iteration. 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.