标题： 在社会正义、伦理与ASA统计实践伦理准则的交叉点上教学 显示英文标题

标题： Teaching at the Intersection of Social Justice, Ethics, and the ASA Ethical Guidelines for Statistical Practice

摘要： 案例研究通常用于教授“伦理学”，但当课程内容集中在公式和证明上时，案例分析及其所需的知识、技能和能力可能会分散注意力。 此外，案例分析通常仅专注于研究问题：获得同意、处理研究团队成员以及研究政策违规等。 并非所有修读定量课程的学生都计划成为研究人员，无论学习者的职业规划如何，数学、统计学、数据科学和计算的伦理实践都是一个关键主题。 虽然将“社会正义”作为“伦理实践”的替代品是不正确的，但“社会正义”这一主题可能对学生和教师都更具吸引力。 本文提出了具体的建议，以在定量课程中融入社会正义内容，这种方式可以限制新知识、技能和能力的负担，同时支持可重复且可操作的评估。 有五种工具可以用来以一种方式将社会正义融入课程中，这种方式也符合将“伦理学”纳入课程的要求；最小化教师创建和批改新材料和作业的负担；最小化学习者发展完成案例分析所需技能集的负担；并最大化伦理内容嵌入学习者对定量课程中所教授知识的认知表示的可能性。 这些工具是：a. 课程开发指南 b. 7任务统计与数据科学流程 c. 美国统计协会统计实践伦理指南 d. 利益相关者分析 e. 6步伦理推理范式 本文讨论了如何在定量课程中使用这些工具。 这些工具和框架提供了结构，并有助于确保对任何课程所做的更改都可以评估，并为学习者生成可操作的评估。 显示英文摘要

摘要： Case studies are typically used to teach 'ethics', but when the content of a course is focused on formulae and proofs, a case analysis and the knowledge, skills, and abilities they require can be distracting. Moreover, case analyses are typically focused narrowly on research issues: obtaining consent, dealing with research team members, and/or research policy violations. Not all students in quantitative courses plan to become researchers, and ethical practice of mathematics, statistics, data science, and computing is an essential topic regardless of the learner's career plans. While it is incorrect to treat 'social justice' as a proxy for 'ethical practice', the topic of 'social justice' may be more interesting to both students and instructors. This paper offers concrete recommendations for integrating social justice content into quantitative courses in ways that limit the burden of new knowledge, skills, and abilities but also support reproducible and actionable assessments. Five tools can be utilized to integrate social justice into a course in a way that also meets calls to integrate 'ethics'; minimizes the burden on instructors to create and grade new materials and assignments; minimizes the burden on learners to develop the skill set to complete a case analysis; and maximizes the likelihood that the ethics content will be embedded in the learners' cognitive representation of the knowledge being taught in the quantitative course. These tools are: a. Curriculum Development Guidelines b. 7-task Statistics and Data Science Pipeline c. ASA Ethical Guidelines for Statistical Practice d. Stakeholder Analysis e. 6-step Ethical Reasoning paradigm This paper discusses how to use these tools in quantitative courses. The tools and frameworks offer structure, and facilitate ensuring that changes made to any course are evaluable and generate actionable assessments for learners.