Teaching Computing to Biologists Through Data Visualization

生物学(61)这个项目支持为科学家和工程师开设的一门独特的计算课程开发和评估课程材料，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。三项教学创新构成了这项开发工作的核心：(1)通过使用数据而不是公式来教授计算。MatLab和其他计算科学课程一般都是针对高级学生进行一些数学培训。这类课程通常一开始就解释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让这些学生使用MatLab可视化并探索以简单列表和表格形式呈现的有意义的数据集。通过使用真实数据而不是抽象的数学公式，学生们很快就会意识到计算在他们的学科中的实际好处和核心作用。(2)采用混合式教学方法进行编程教学。本课程侧重于编程任务，这些任务可以在一个学期的课程中轻松掌握，并在后续课程中有用。利用诸如matlab的绘图工具之类的设施，以混合的方式教授编程技能。MatLab的绘图工具允许学生通过一个直观的“点击”图形用户界面检查和绘制数据，并向他们展示生成绘图的编程代码。学生被要求修改此代码，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)融合真实科学，突出关联性、互动性和发现性。这些材料尽可能地依赖真实数据，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。创建附注来解释论文的结构、结果是什么，以及数据如何支持结果。这些材料是以网络格式生成的，带有参考和注释的链接。这门课程强调实际操作的数据分析，并在他们的学习计划的早期被要求，对理科学生的量化技能有重大的影响。此外，课程内容和教材通过开放源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。

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).