Enhancement of Quantitative Science in Biology Curricula

加强生物学课程中的定量科学

• 批准号: 7870451
• 负责人: STEPHEN B ALEY
• 金额: $ 24.43万
• 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/7870451
• 关键词: Active Learning; Area; Arizona; Biochemistry; Bioinformatics; Biological; Biology; Biomedical Research; Case Study; 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; Perception; Pre-Post Tests; Research; Research Personnel; Science; Sister; 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（由申请人提供）：随着生物学和生物医学研究对大型数据集（如生物信息学或CIS系统）的依赖性越来越大，越来越明显的是，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有很强的背景。相比之下，刻板的生物学学生往往是STEM学科中数学准备最差的学生，这种情况有时会被传统的生物学课程所迎合，这些课程几乎消除了该领域的定量方面。我们建议开始扭转这种情况，通过开发一个强大的，定量的框架，并与数学科学和计算机科学的整体跨学科联系修订生物学课程。特别是，我们将修改和协调一致的分析方法，包括计算机建模，统计分析，定量表示和相关案例研究，在积极的学习环境中，在生物学，数学，统计学和计算机科学的前两年的所有生物学学位所需的核心课程。此外，我们将开发上师衔接课程，以采取感兴趣的学生从三个学科中的每一个，并提供足够的交叉培训，让他们继续在跨学科领域，如生物信息学的研究生课程。所有课程的修订将由一个跨学科的教师小组进行审查，以确保所有领域的一致性和准确性。我们还将为所有参与的教师提供主动学习和其他教学技术的培训。我们希望这些方法能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。