SIAM Review, Volume 65, Issue 4, Page 1152-1170, November 2023.
Quantitative methods and mathematical modeling are playing an increasingly important role across disciplines. As a result, interdisciplinary mathematics courses are increasing in popularity. However, teaching such courses at an advanced level can be challenging. Students often arrive with different mathematical backgrounds, different interests, and divergent reasons for wanting to learn the material. Here we describe a course on stochastic processes in biology delivered between September and December 2020 to a mixed audience of mathematicians and biologists. In addition to traditional lectures and homework, we incorporated a series of weekly computational challenges into the course. These challenges served to familiarize students with the main modeling concepts and provide them with an introduction on how to implement the concepts in a research-like setting. In order to account for the different academic backgrounds of the students, they worked on the challenges in small groups and presented their results and code in a dedicated discussion class each week. We discuss our experience designing and implementing an element of problem-based learning in an applied mathematics course through computational challenges. We also discuss feedback from students and describe the content of the challenges presented in the course. We provide all materials, along with example code for a number of challenges.

中文翻译：

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将计算挑战纳入随机过程的多学科课程

SIAM Review，第 65 卷，第 4 期，第 1152-1170 页，2023 年 11 月。
定量方法和数学建模在跨学科中发挥着越来越重要的作用。因此，跨学科数学课程越来越受欢迎。然而，教授此类高级课程可能具有挑战性。学生往往带着不同的数学背景、不同的兴趣以及想要学习材料的不同原因。在这里，我们描述了 2020 年 9 月至 12 月期间向数学家和生物学家等混合受众提供的一门关于生物学随机过程的课程。除了传统的讲座和作业之外，我们还在课程中纳入了一系列每周计算挑战。这些挑战有助于学生熟悉主要建模概念，并向他们介绍如何在类似研究的环境中实施这些概念。为了考虑到学生的不同学术背景，他们以小组形式应对挑战，并在每周的专门讨论课上展示他们的结果和代码。我们讨论了通过计算挑战在应用数学课程中设计和实施基于问题的学习要素的经验。我们还讨论学生的反馈并描述课程中提出的挑战的内容。我们提供所有材料以及应对许多挑战的示例代码。