教学再现性的意见实践：动机，指导教学和实践
Opinionated practices for teaching reproducibility: motivation, guided instruction and practice
摘要：
在不列颠哥伦比亚大学的数据科学课程中，我们将数据科学定义为研究，开发和实践可复制和可审计的过程，以从数据中获得洞察力。虽然可重复性是我们定义的核心，但大多数数据科学学习者在进入该领域时都考虑到了数据科学的其他方面，例如预测建模，这通常是新手最感兴趣的话题之一。这一事实，以及目前在数据科学中使用的行业标准再现性工具的高度技术性，在数据科学课堂上教授再现性方面提出了挑战。简单地说，学生们并没有内在的动机去学习这个话题，而且对他们来说，这不是一个容易学习的话题。数据科学教育者能做什么？在几次迭代的教学课程中，我们发现提供额外的动机，指导性的指导和大量的实践是有效教授这一具有挑战性但重要的主题的关键。在这里，我们展示了我们如何深入激励，有效指导和提供充足的实践机会，让数据科学专业的学生有效地参与到这个主题的学习中来。
英文摘要：
In the data science courses at the University of British Columbia, we define data science as the study, development and practice of reproducible and auditable processes to obtain insight from data. While reproducibility is core to our definition, most data science learners enter the field with other aspects of data science in mind, for example predictive modelling, which is often one of the most interesting topic to novices. This fact, along with the highly technical nature of the industry standard reproducibility tools currently employed in data science, present out-of-the gate challenges in teaching reproducibility in the data science classroom. Put simply, students are not as intrinsically motivated to learn this topic, and it is not an easy one for them to learn. What can a data science educator do? Over several iterations of teaching courses focused on reproducible data science tools and workflows, we have found that providing extra motivation, guided instruction and lots of practice are key to effectively teaching this challenging, yet important subject. Here we present examples of how we deeply motivate, effectively guide and provide ample practice opportunities to data science students to effectively engage them in learning about this topic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
