Enhancement of Quantitative Science in Biology Curricula

加强生物学课程中的定量科学

• 批准号: 7124120
• 负责人: STEPHEN B ALEY
• 金额: $ 26.68万
• 依托单位: UNIVERSITY OF TEXAS EL PASO
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7124120
• 关键词: Active Learning; Area; Arizona; Biochemistry; Bioinformatics; Biological; Biology; Biomedical Research; Case Study; Class; Classification; Computer Simulation; Computers; Data Set; Dependence; Development; Discipline; Educational Curriculum; Educational workshop; Enrollment; Environment; Evaluation; Faculty; Fertilization; Focus Groups; Goals; Grant; Knowledge; Mathematics; Measurable; Measures; Methods; Microbiology; Modeling; Numbers; Perception; Pre-Post Tests; Research; Research Personnel; Science; Sister; Statistical Models; Statistics, Other; Structure; Students; Surveys; System; Techniques; Testing; Time; Training; Travel; Universities; career; computer program; computer science; design; instrument; interest; programs; skills; statistics; stem

DESCRIPTION (provided by applicant): With the increasing dependence of biological and biomedical research on large data sets such as those for bioinformatics or CIS systems, it is becoming apparent that the well-trained biologist needs to have a strong background in computation and computer modeling, statistics and other quantitative analysis. In contrast, the stereotypical biology student tends to be the least well mathematically prepared of students in the STEM disciplines, a situation sometimes catered to by traditional Biology courses that all but eliminate quantitative aspects of the field. We propose to begin to reverse this situation by developing a revised Biology curriculum with a strong, quantitative framework and with integral interdisciplinary ties to Mathematical Science and Computer Science. In particular, we will revise and coordinate a consistent analytical approach, including computer modeling, statistical analysis, quantitative representation, and relevant case studies in an active learning environment in all Biology degree required core classes of the first two years in Biology, Mathematics, Statistics, and Computer Science. In addition, we will develop upper division bridging courses to take interested students from each of the three disciplines and provide sufficient cross-training for them to continue graduate studies in an interdisciplinary field such as Bioinformatics. All course revisions will be reviewed by an interdisciplinary panel of faculty for consistency and accuracy in all fields. We will also provide training in active learning and other pedagogical techniques for all involved faculty. We expect these approaches to result in a greater number of students better prepared for research careers in Biology and Biomedical Science.

描述(申请人提供)：随着生物和生物医学研究对诸如生物信息学或独联体系统等大数据集的日益依赖，训练有素的生物学家需要在计算和计算机建模、统计学和其他定量分析方面拥有强大的背景变得越来越明显。相比之下，在STEM学科的学生中，刻板印象中的生物学学生往往是数学准备最差的，传统的生物学课程有时会迎合这种情况，这些课程几乎消除了该领域的量化方面。我们建议开始扭转这种情况，制定一个修订的生物学课程，具有强大的量化框架，并与数学科学和计算机科学建立完整的跨学科联系。特别是，我们将修订和协调一致的分析方法，包括在主动学习环境中的计算机建模、统计分析、量化表示和相关的案例研究，在所有生物学学位要求的头两年的生物学、数学、统计学和计算机科学核心课程中进行案例研究。此外，我们会发展高级衔接课程，招收三个学科中每一个有兴趣的学生，并为他们提供足够的交叉训练，让他们在生物信息学等跨学科领域继续研究生课程。所有的课程修订都将由一个跨学科的教员小组进行审查，以确保所有领域的一致性和准确性。我们还将为所有相关教职员工提供主动学习和其他教学技巧方面的培训。我们预计这些方法将使更多的学生为生物学和生物医学科学的研究生涯做好更好的准备。