Teaching Computing to Biologists Through Data Visualization

通过数据可视化向生物学家教授计算

• 批准号: 0837248
• 负责人: Kay Robbins
• 金额: $ 14.66万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-12-15 至 2012-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0837248&HistoricalAwards=false
• 关键词: Teaching; Computing; Biologists; Through; Data

Biology (61)This project supports the development and assessment of curricular materials for a unique Computation for Scientists and Engineers course that uses MATLAB to teach programming to biologists through data analysis and visualization. Three teaching innovations form the central core of this development effort: (1) Teach computation by working with data rather than by working with formulas. MATLAB and other computational science courses generally target advanced students with some mathematical training. Such courses typically begin by explaining how MATLAB performs vector and matrix operations - an approach that is obviously inappropriate for many students, especially those with limited math and logic skills. A more engaging alternative is to ask these students to use MATLAB to visualize and explore meaningful data sets that are presented as simple lists and tables. By working with real data instead of abstract mathematical equations, students quickly appreciate the practical benefits and central role of computing in their discipline. (2) Use a hybrid approach to teaching programming. The course focuses on programming tasks that can be easily mastered in a single semester course and are useful in subsequent courses. Facilities such as MATLAB's plottools are exploited to teach programming skills in a hybrid fashion. MATLAB's plottools allow students to examine and plot data through an intuitive "point and click" GUI interface, and show them the programming code that produced the plot. Students are asked to modify this code in order to visualize the data differently or to explore completely different data sets. (3) Integrate real science to highlight relevance, interaction and discovery. The materials rely on real data as much as possible, and several significant case studies are developed in support of the curriculum. Important scientific papers are used for examples. Side notes are created to explain how the paper is structured, what the results are, and how the data supported the results. These materials are generated in web format with links to the references and notes. By emphasizing hands-on data analysis and being required early in their program of study, this course has a significant impact on the quantitative skills of science students. Furthermore, the curricular content and teaching materials are made broadly available to other institutions using an open source model and linkage to the National Science Digital Library (NSDL).

生物学（61）该项目支持开发和评估课程材料，用于科学家和工程师的独特计算课程，该课程使用MATLAB通过数据分析和可视化向生物学家教授编程。三个教学创新形成了这一发展努力的核心：（1）通过使用数据而不是使用公式来教授计算。MATLAB和其他计算科学课程通常针对具有一定数学训练的高级学生。这类课程通常开始解释MATLAB如何执行向量和矩阵运算-这种方法显然不适合许多学生，特别是那些数学和逻辑技能有限的学生。一个更吸引人的选择是要求这些学生使用MATLAB来可视化和探索以简单列表和表格形式呈现的有意义的数据集。通过使用真实的数据，而不是抽象的数学方程，学生们很快就能体会到计算在他们的学科中的实际好处和核心作用。(2)使用混合方法来教授编程。该课程侧重于编程任务，可以很容易地掌握在一个学期的课程，并在后续课程有用。利用MATLAB的plottools等设施以混合方式教授编程技能。MATLAB的plottools允许学生通过直观的“点击”GUI界面检查和绘制数据，并向他们展示生成图的编程代码。要求学生修改此代码，以便以不同的方式可视化数据或探索完全不同的数据集。(3)整合真实的科学以突出相关性、互动和发现。这些材料尽可能依赖于真实的数据，并开发了几个重要的案例研究来支持课程。重要的科学论文被用作例子。创建边注来解释论文的结构，结果是什么，以及数据如何支持结果。这些材料以网络格式生成，并附有参考资料和说明的链接。通过强调实践数据分析，并在学习计划的早期要求，本课程对理科学生的定量技能产生了重大影响。此外，课程内容和教学材料通过开放源代码模式和与国家科学数字图书馆（NSDL）的链接向其他机构广泛提供。