Grunnleggende konsepter
This paper presents a practical framework for integrating ethical reasoning into quantitative courses by leveraging the contexts of assumptions, approximations, and applications, and utilizing learning outcomes, stakeholder analysis, and ethical guidelines.
Sammendrag
This article proposes a practical framework for integrating ethical reasoning into quantitative courses, particularly mathematics. It emphasizes the importance of teaching ethical reasoning as a learnable skill rather than focusing on abstract "ethics."
Key Highlights:
- Learning Outcomes (LOs): The article advocates for using LOs based on Bloom's taxonomy to guide instruction and assessment of ethical reasoning. It provides examples of LOs at different cognitive complexity levels, scaffolding learning throughout a course.
- Contexts for Integration: The paper identifies three common contexts in quantitative courses for introducing ethical considerations: assumptions, approximations, and applications. It suggests prompting students to analyze potential consequences when these aspects are not met or justified.
- Stakeholder Analysis: A key tool for teaching ethical reasoning is the Stakeholder Analysis template. This helps students identify individuals or groups impacted by decisions and analyze potential harms and benefits.
- Ethical Guidelines: The article emphasizes the use of existing ethical guidelines from organizations like the ASA and ACM, as well as the proposed Mathematics Ethical proto-Guidelines. These provide a framework for ethical conduct in quantitative fields.
- Ethical Reasoning KSAs: The six-step ethical reasoning process is presented, emphasizing the importance of identifying stakeholders, understanding guidelines, evaluating alternatives, and justifying decisions.
Practical Application:
The article provides concrete examples of assignments and activities that instructors can adapt to their courses. It suggests using case studies, analyzing real-world scenarios, and engaging students in discussions about the ethical implications of mathematical concepts and applications.
Significance:
By integrating ethical reasoning into quantitative courses, educators can equip students with the skills to navigate ethical challenges in their future careers. This is crucial as mathematics, statistics, and data science play increasingly significant roles in various fields, impacting individuals, organizations, and society as a whole.
Statistikk
The ACM Code of Ethics has 24 narrative elements.
The ASA Ethical Guidelines for Statistical Practice have 72 elements.
The Mathematics Ethical proto-Guidelines comprises 44 items.
Sitater
“Ethics” is recommended content for statistics and data science curricula in higher education (e.g., American Statistical Association Undergraduate Guidelines Workgroup 2014; DeVeaux et al. 2017; National Academies 2018; Association of Computing Machinery Data Science Task Force, 2021)
“Upon entry into practice, all professionals assume at least a tacit responsibility for the quality and integrity of their own work and that of colleagues. They also take on a responsibility to the larger public for the standards of practice associated with the profession.” (Golde & Walker, 2006: p. 10)
“Learning outcomes are statements of the knowledge, skills and abilities individual students should possess and can demonstrate upon completion of a learning experience or sequence of learning experiences.” (Stanford University, n. d.)