Computational Thinking in Life Science Education

We join the increasing call to take computational education of life science students a step further, beyond teaching mere programming and employing existing software tools. We describe a new course, focusing on enriching the curriculum of life science students with abstract, algorithmic, and logical thinking, and exposing them to the computational “culture.” The design, structure, and content of our course are influenced by recent efforts in this area, collaborations with life scientists, and our own instructional experience. Specifically, we suggest that an effective course of this nature should: (1) devote time to explicitly reflect upon computational thinking processes, resisting the temptation to drift to purely practical instruction, (2) focus on discrete notions, rather than on continuous ones, and (3) have basic programming as a prerequisite, so students need not be preoccupied with elementary programming issues. We strongly recommend that the mere use of existing bioinformatics tools and packages should not replace hands-on programming. Yet, we suggest that programming will mostly serve as a means to practice computational thinking processes. This paper deals with the challenges and considerations of such computational education for life science students. It also describes a concrete implementation of the course and encourages its use by others.

我们响应日益增长的行业呼吁，将生命科学专业学生的计算教育推向更深层次——不再局限于单纯教授编程与使用现有软件工具。本文介绍一门全新课程，旨在通过抽象思维、算法思维与逻辑思维的训练丰富生命科学专业学生的课程体系，并帮助他们接触计算领域的"学科文化"。本课程的设计框架、结构安排与教学内容，参考了该领域的最新研究实践、与生命科学研究者的合作经验，以及我们自身的教学积累。具体而言，此类高效课程应满足以下三点：(1) 预留充足时间对计算思维过程进行显性梳理与反思，避免陷入单纯的实践教学误区；(2) 聚焦离散概念而非连续概念展开教学；(3) 将基础编程设为先修课程，使学生无需将精力耗费在基础编程问题上。我们强烈建议，切勿仅通过使用现有生物信息学工具与软件包来替代实操编程；同时应明确，编程主要应作为实践计算思维的载体而非最终目标。本文探讨了面向生命科学学生的此类计算教育所面临的挑战与考量要点，并详细阐述了该课程的具体落地实施方案，同时呼吁其他教育者借鉴推广该课程。