Instructional Design for Ethical Reasoning in the Digital Age

The effects of globalization and corresponding Neoliberalist attitudes have contributed to the emergence of a 4th Industrial Revolution (4IR) that is characterized by an increased reliance on Big Data Analytics (BDA) in society, politics, policy and industry (Skilton et al., 2018). Without minimizing its positive contributions, similar to past industrial revolutions, rapid societal changes have resulted in negative ramifications for individuals, marginalized societal groups, and ecologies (Benjamin, 2019, Noble, 2018). For instance, in the education field, advances in generative AI have created a forced paradigm shift for educators and students alike, forcing us to redefine what it means to teach and learn on the fly. Similarly, the use of facial recognition software, while convenient when logging into our Iphones and detecting known criminals, contributes to the incarceration cycle in US prisons, whereby black and brown folks are incarcerated (often wrongly) at higher rates (Benjamin, 2019; O’Neil, 2016). Importantly, those who develop the algorithms that are behind these systems (data scientists), are rarely members of the populations who are more often processed by them (i.e. the masses). In addition, data scientists more often come from privileged positions in society, resulting in their inability to recognize instances of, or the potential for, oppression (D’Ignazio et al., 2020). As a result, data science designs more often reflect the dominant perspectives, experiences, and values of the privileged creators, at the expense of nondominant identities and viewpoints (D’Ignazio et al., 2020; Noble, 2018).

Given the documented negative effects of BDA and its permeating influence in society, it is argued that STEM educators, (especially those in mathematics and statistics) have a distinct responsibility to combat forms of oppression analogous to the 4IR (Matthews, 2019). In particular, we argue that citizens must develop an ethical mathematics consciousness (EMC) that human beings do mathematics; thus, there are potential ethical dilemmas and implications of mathematical work which may affect entities at the individual, group, societal, and/or environmental level. To support STEM educators in this endeavor, the proposed presentation aims to introduce an analytic and design framework for developing instructional materials that may foster students’ ethical reasoning in mathematics and data science, but that can be adapted for disciplines outside of STEM. 

Impetus for this Work

The societal impact of BDA has catalyzed conversations related to the “need for ethics” in mathematics education. But what kind of ethics? The literature on ethical reasoning in mathematics education is fragmented in the sense that ethics and social justice are often discussed separately. However, as Atweh et al., (2009) suggests, the foremost concern in ethics is our relationship with and for others, therefore ethics actually serves as the foundation for concerns of social justice (Atweh et al., 2009). While this may be true from the perspective of relational ethics, other major normative theories that characterize the ethics of decision making neither show a concern for relations nor justice. As a result, scholars argue that traditional normative ethical frameworks are insufficient for protecting against the repercussions of the data science industry due to their lack of attention to its sociopolitical elements (D’Ignazio  et al., 2020). Therefore, we cannot simply argue that students need to learn to be ethical in a general sense since our ethics are a product of our culture, communities, experiences, etc. which are naturally diverse and often competing (Brown University, 2013). Rather, we must champion ethical perspectives that lend themselves to empathy, justice, and sociopolitical thought. 

EMC Analytic Framework 

The EMC Analytic Framework emerged from an ongoing Design Research Project (Bakker et al., 2014) intended to cultivate students' ethical reasoning in mathematics and data science. Its purpose is to inform the researcher about individuals’ probable EMC in a variety of given data science contexts in order to design mathematics and data science curriculum materials grounded in a Feminist ethic of care and social response-ability (Noddings, 1986; Puka, 2005). As an analytic tool, the EMC Framework helps us to identify the forms of ethical reasoning that individuals exhibit and how that may change over time and through instruction. As a design aid, it helps the instructional designers identify diverse ethical perspectives and make conjectures about potential instructional problems that can challenge problematic ethical perspectives, motivating students to develop a feminist ethic of care and social response-ability.

 To establish the framework, we researched normative ethical perspectives and aligned their respective forms of ethical reasoning with plausible levels of critical consciousness (Freire, 1970/2018). Importantly, developing an ethical disposition requires robust sociopolitical understandings as well as knowledge of the structures and mechanisms which contribute to social injustices, including mathematical processes, products, and ways of knowing. In other words, it requires a critical consciousness of the ways in which mathematics and mathematical processes serve to disenfranchise some and privilege others (Authors., 2020).

 

Presentation Goals

Report: The presentation will report on the development and modification of the EMC Framework across three pilot studies (with middle and high school mathematics students in the US, and preservice mathematics teachers in the US and Sweden) (Authors, 2021; Authors, 2020; Author, 2022; 2023; under review; submitted). It will then introduce the designed Ethical Data Science (EDS) course for high school juniors and seniors grounded in a feminist ethic of care and social responsibility (Author, submitted). Findings from the pilot studies that served to guide the design of the EDS course, and findings from the retrospective analysis of the EDS course that fueled our design of an Ethical Decision-Making in Data Science Protocol will be discussed in reference to the offered instructional materials.  

Activity: Participants will be given a designed ethical task used in the EDS course and pilot studies. They will respond to the tasks individually. After being introduced to the EMC framework, they will use it to analyze their reasoning in the given task. They will then be given the designed Ethical Decision-Making in Data Science Protocol designed as a result of our retrospective analysis to encourage critically conscious ethical decision making. They will complete the task again using the protocol. Participants will discuss in small groups how use of the protocol may or may not have changed their solution to the presented dilemma. 

Discussion: We will convene as a group to discuss implications for instructional design and engage in Q&A. I will end the session with an overview of “what worked” for us in our designs. Participants will leave with an empirically researched design framework for promoting critically conscious ethical reasoning in data science, a designed protocol for making ethical decisions in data science, and several example ethical tasks that they may choose to incorporate into their own work. 

 

Significance

This study is significant for the OLC community in that it provides  product- and theory-oriented guidance related to the development of ethical mathematicians and data scientists. Currently, little research exists on the development of ethical mathematicians beyond the exploration of social injustices that have already occurred (e.g. Critical Mathematics Education, Skovsmose, 1994). Rather, mathematics must be promoted as a way of thinking about, understanding, and formulating solutions to present and future societal phenomena, that both permeates into daily life and a myriad of industries, especially those which govern our world. Furthermore, attempts at developing an ethical STEM education are primarily theoretical and typically fail to consider the range of normative ethical perspectives that diverse individuals hold. In contrast, the reported framework supports the consideration of students’ diverse ethical perspectives to promote a transformative educational experience that centers the responsible creation of mathematical products in order to promote a just and equitable future (Atweh et al., 2009). 

 

References

Atweh, B., Brady, K. (2009). Socially response-able mathematics education: Implications of an ethical approach. Eurasia Journal of Mathematics, Science & Technology Education, 5(3), 267-276. https://doi.org/10.12973/ejmste/75278

Bakker, A., & Van Eerde, H. A. A. (2014). An introduction to design-based research with an example from statistics education. In A. Bikner Ahsbahs, C. Knipping, & N. Presmeg (Eds.), Doing qualitative research: methodology and methods in mathematics education. Springer. https://doi.org/10.1007/978-94-017-9181-6_16

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press

Boylan, M. (2016). Ethical dimensions of mathematics education. Educational Studies in mathematics, 92, 395–409. https://doi.org/10.1007/s10649-015-9678-z

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. Cambridge, MA: The MIT Press.

Freire, P. (1070/2018). Pedagogy of the oppressed. London: Penguin Books.

Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. 

Noddings, N. (1988). An ethic of caring and its implications for instructional arrangements. American journal of education, 96(2), 215-230.

O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. London: Penguin Books

Puka, B. (2005). Teaching ethical excellence: Artful response-ability, creative integrity, character opus. Liberal Education, 91(3/4), 22–25.

Authors  (2021).

Authors  (2022).

Authors  (2023).

Authors (in press). 

Authors (submitted). 

Skilton, M., & Hovsepian, F. (2018). The 4th industrial revolution. Springer Nature.

Skovsmose, O. (1994). Towards a philosophy of critical mathematics education. Dordrecht: Kluwer Academic Publishers. 

Authors (2020).