Remington Jacob M, Ferrell Jonathon B, Zorman Marlo, Petrucci Adam, Schneebeli Severin T, Li Jianing
Department of Chemistry, The University of Vermont, Burlington, VT 05403.
Biophysicist (Rockv). 2020 Aug;1(2). doi: 10.35459/tbp.2019.000140. Epub 2020 Aug 13.
Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.
计算机硬件和软件的最新进展，尤其是机器学习库的可用性，使得将基于数据的主题（如机器学习）引入本科和/或研究生水平的生物物理课程成为可能。然而，对于生物物理专业的高年级学生而言，教授机器学习存在诸多实际挑战，因为他们通常没有丰富的计算背景。为了克服这些挑战，我们开展了一项教育研究，包括课程主题设计、教学工具以及对学生学习的评估，以开发新方法，将机器学习基础纳入现有的生物物理选修课程，并让学生参与练习，解决跨学科领域的问题。总体而言，我们观察到学生对学习和应用机器学习算法来预测分子特性充满浓厚兴趣。值得注意的是，学生的反馈表明，必须谨慎确保学生为理解使用机器学习算法所需的数据驱动概念和基本编码方面做好准备。这项工作为未来将机器学习与生物物理课程中的任何现有课程相结合的教学方法建立了一个框架，同时也指出了教育工作者和学生可能面临的关键挑战。
