论文标题
激励数据科学专业的学生参与并学习
Motivating Data Science Students to Participate and Learn
论文作者
论文摘要
数据科学教育越来越多地涉及人类主题和社会问题，例如隐私，道德和公平。数据科学家需要具备技能，以应对围绕其数据科学工作的社会背景的复杂性。在本文中，我们提供了有关如何构建我们的数据科学课程的见解，以便他们激励学生深入了解社会背景的材料，并倾向于将批判性思维技能持续增长的对话类型。特别是，我们描述了一种名为“参与投资组合”的新颖评估工具，该工具是由促进学生自主权，自我反思和建立学习社区的框架的动机。我们比较了学生在实施此评估工具之前和之后的参与，我们的结果表明，该工具增加了学生的参与，并帮助他们朝着课程学习目标发展。
Data science education is increasingly involving human subjects and societal issues such as privacy, ethics, and fairness. Data scientists need to be equipped with skills to tackle the complexities of the societal context surrounding their data science work. In this paper, we offer insights into how to structure our data science classes so that they motivate students to deeply engage with material about societal context and lean into the types of conversations that will produce long lasting growth in critical thinking skills. In particular, we describe a novel assessment tool called participation portfolios, which is motivated by a framework that promotes student autonomy, self reflection, and the building of a learning community. We compare student participation before and after implementing this assessment tool, and our results suggest that this tool increased student participation and helped them move towards course learning objectives.
