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A Biomathematical Learning Enhancement Network for Diversity (BLEND)

A Biomathematical Learning Enhancement Network for Diversity (BLEND)
多样性生物数学学习增强网络(BLEND)
批准号:
0634598
负责人:
Gregory Goins
金额:
$24.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-15 至 2009-08-31

项目摘要

项目成果

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中文摘要
翻译
尽管历史上少数族裔大学(HMU)的生物和数学项目一直培养出大量的STEM学士学位,但需要更多的创新项目来增加继续获得博士学位的本科生数量。挑战是汇集大量跨学科研究合作和伙伴关系,以解决数学和生物领域中代表性较低的少数族裔成就差距的根本根源。北卡罗来纳A&T州立大学的生物数学学习增强网络多样性(BLEND)项目是由教员组成的跨部门联盟提出的,这些教员是变革的早期采用者,要求学生为生物和数学的研究生学习做好准备。Blend项目将提供一个物理和虚拟的智力环境,学生可以在其中找到认同感、归属感、责任感,最重要的是成就感,为他们在生物数学研究生涯中扮演领导和服务的角色做好准备。Blend项目的总体目标是培养出为生物数学研究的跨学科性质做好突出准备的本科生。为了实现这一目标,Blend项目将(1)将研究和课堂经验联系起来,(2)在数学和生物的接口上提供联合指导,(3)扩大对生物数学培训和研究的吸引力。我们的核心基因组研究集群将为学生提供一个自然的机会,让他们从数学和计算的角度探索、学习和参与生物学问题,同时更深入地了解基因组研究。这些研究项目的中心主题是基因组测序和蛋白质结构预测中的高通量数据收集方法。研究活动将集中在非生物植物的逆境耐受策略和识别与逆境相关的基因和途径。学生们将从这些研究活动中了解到分子、生化和生理机制之间的联系,这些机制使高等植物能够应对干旱、盐分和极端温度等非生物胁迫条件。这些研究活动是背景驱动的,目的是提供一种创新的学习文化,将本科生与生物和数学之间有洞察力的互动联系起来。该项目将推进一种前瞻性的思维模式,以激励未被充分代表的少数群体在数学和生物学的交界处追求职业。
英文摘要
Although biology and mathematics programs at Historically Minority Universities (HMUs) have consistently produced high numbers of STEM baccalaureate degrees, more innovative programs are needed to increase the number of undergraduates who go on to earn the Ph.D. The challenge is to bring together a critical mass of interdisciplinary research collaborations and partnerships that address basic root origins of achievement gaps for underrepresented minorities in mathematics and biology. The Biomathematics Learning Enhancement Network for Diversity (BLEND) project at North Carolina A&T State University was conceptualized by an interdepartmental alliance of faculty who are early adopters of transformational change required to prepare students for graduate study at the interface of biology and mathematics. The BLEND project will supply both a physical and virtual intellectual setting where students may find a sense of identification, belonging, responsibility, and most importantly, achievement that prepares them for roles of leadership and service in biomathematical research careers. The overall goal of the BLEND project is to produce undergraduate students outstandingly prepared for the interdisciplinary nature of biomathematical research. To accomplish this goal, the BLEND project will (1) link research and classroom experiences, (2) provide joint-mentoring at the interface of mathematics and biology, and (3) broaden the appeal to biomathematics training and research. Our Core Genomics Research Cluster will provide a natural opportunity for students to explore, learn, and engage problems in biology from a mathematical and computational perspective while gaining deeper insight into genomics research. The central theme of the research projects is high-throughput data collection methods in genome sequencing and protein structure prediction. Research activities will focus on abiotic plant stress tolerance strategies and identification of stress-relevant genes and pathways. Students will emerge from these research activities with a coherent picture of links between molecular, biochemical, and physiological mechanisms that enable higher plants to cope with abiotic stress conditions such as drought, salinity, and temperature extremes. These research activities are context-driven and purposed to provide an innovative learning culture that connects undergraduates with insightful interactions between biology and mathematics. This project will advance a forward-thinking model for motivating underrepresented minorities to pursue professions at the interface of mathematics and biology.
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会议论文
NSF Engines Development Award: Creating climate-resilient opportunities for plant systems (NC)
NSF INCLUDES: Building Diverse and Integrative STEM Continua Using Socio-environmental Systems In and Out of Neighborhoods (DISCUSSION)
UBM Group - An Integrative Biomathematical Learning and Empowerment Network for Diversity (iBLEND)
Improving and Expanding Student Experiences in Introductory Biology Courses with Online Learning Modules
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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