Local Classrooms,Global Technologies: Toward the Integration of Sociotechnical Macroethical Issues Into Teacher Education

Abstract

Discussions of ethics within in-service teacher education tend to focus on microethical concerns (e.g., discipline) that deal with decision making at interpersonal levels. Issues concerning educational technology are no exception. Yet, as teachers choose and are expected to integrate technological devices (e.g., laptops) and sociotechnical systems (e.g., learning management systems) into pedagogical practices, their classrooms and schools may become implicated in macroethical issues (e.g., electronic waste) that reach beyond the local consequences of their direct actions. Necessitated by tight couplings of technology and education, this article presents the concept of macroethics for teacher educators by grounding it in three areas of concern: obsolescence, automation, and big data. These three areas offer opportunities to make economic and environmental issues more central to case studies on technology in teacher education. At the same time, teacher educators will need to put emphasis on critical reflection and collective action in units on macroethics since the limited impact of individual decision-making on these issues may put teachers in double binds (i.e., dilemmas with contradictory demands).

Keywords

macroethics teacher education technology double bind

Introduction

The integration of technology into schools is both top-down and bottom-up (Selwyn, 2017). From the top-down, administrators, politicians, and nongovernmental organizations seek innovative and efficient uses of learning management systems and networked infrastructures to support 21st century skills and technocentric curriculum reforms. From the bottom-up, teachers, paraprofessionals, and instructional coaches seek out gamified curriculum connections and educational construction kits to support personalized and differentiated pathways for student achievement. From this multidirectional blending of technologies comes a proliferation of new ethical concerns for teachers to confront and negotiate: plagiarism, cyberbullying, access to age-inappropriate content, and so on. While required ethics courses in teacher education programs are becoming less common (Maxwell & Schwimmer, 2016), research, scholarship, and institutional policies that seek to help address ethical concerns surrounding technology design and use are growing in numbers and importance (e.g., Association for Educational Communications and Technology, 2012; Kafai et al., 2007; Spector, 2016a).

In education generally, ethics policies and scholarship tend to focus on microethics (e.g., discipline, truthfulness, grading consistency, etc.) that deal with decision-making and its immediate consequences at interpersonal levels that pertain to day-to-day classroom needs (Strike & Soltis, 2016). Much less focus is placed on macroethical concerns that are about social and collective responsibility beyond individuals’ immediate proximity. In their survey of professional ethics for teachers, Maxwell and Schwimmer (2016, p. 357) explained, “The professionalism agenda in ethics education, for example, is routinely critiqued for obscuring crucial macroethical issues in professional practice because it focuses narrowly on teachers as individual actors responsible for their own conduct and immediate well-being of their clients.” Indeed, the Association for Educational Communications and Technology is explicit about the “ethical practice of facilitating learning and improving performance by creating, using, and managing appropriate technological processes and resources” in their definition of educational technology (as cited in Spector, 2016a, p. 1003), but the focus on microethics persists within the association and the educational technology community more generally.

This is apparent, for example, in Spector’s (2016b) five principles of the “Educratic Oath,” developed for educational technology design and implementation:

1. Do nothing to impair learning, performance, and instruction.

2. Do what you can to improve learning, performance, and instruction.

3. Base your actions on evidence that you and others have gathered and analyzed.

4. Share the principles of learning, performance, and instruction that you have learned with others.

5. Respect the individual rights of all those with whom you interact. (p. 17)

Only being interpersonally accountable is most exemplified in the last principle, but a closer look reveals how proximity shapes each one. When larger macroissues are introduced the oath becomes more complicated. How, for instance, is a teacher or technologist in the United States supposed to “do nothing to impair learning, performance and instruction” when using computers from companies that include child labor in their production chains (Amnesty International, 2017)? It may be that considering issues like child labor, geopolitical conflicts, structural racism, and the environmental devastation from extracting the rare earth minerals used in computing technologies appear beyond the scope of U.S. teachers’ abilities to create more ethical alternatives. However, it is precisely because such seemingly abstract issues are immediately implicated in technology design and use that they require close ethical attention and nuanced consideration by educators.

Case studies, which can be evaluated through an array of different consequentialist (e.g., utilitarian), nonconsequentialist (e.g., Kantian), and relational (e.g., ethics of care) ethical approaches in the classroom, are commonly used to help teachers and students understand and prepare to confront educational dilemmas. Yet the lack of literature on the macroethics of technology in teacher education makes it hard to find or create such resources. For these reasons, this article introduces the concept of macroethics by grounding it in three areas of concern that are relevant to the intersection of education and technology: (1) obsolescence, (2) automation, and (3) big data. Rather than explore these issues through specific ethical frameworks, we frame them in terms of macroethics to support making social, economic, and environmental issues more central to case studies on technology in teacher education. Based on personal experience, it is acknowledged that the limited impact of individual decision-making on macroethical issues may make teachers feel as if they are in a double bind, that is, a dilemma with multiple undesirable choices or contradictory demands (Bateson, 1972). Therefore, when considering how to design case studies that include macroethical concerns alongside the microethical it is suggested that teacher educators put an emphasis on critical reflection and collective action.

An Introduction to Macroethics

While ethics and morality are often used interchangeably, ethics may best be understood as a branch of philosophy that studies morality (i.e., the rules and norms about right and wrong, good and bad, just and unjust, etc.). Most common today, philosophers have three different categories for ethics: (1) metaethics that explore the origins and nature of moral standards, (2) normative ethics that demarcate right and wrong conduct for individual behaviors, and (3) applied ethics that are dilemmas grounded in real-world contexts and difficult to resolve via individual action (Spector 2016a, p. 1005). The ethical frameworks and standards that are most commonly found in professional organizations (e.g., National Education Association) and scholarly communities tend to be normative. They focus on the actions, intentions, and outcomes of individual decision-making. For the purposes of this article, these ethical dilemmas are called microethics.

Microethics are important for helping teachers and technologists navigate nuanced professional relationships and complicated institutions. However, as Cox (2013) argued,

Ethical theories generally apply at the individual transaction level—they help us evaluate one person’s interactions with another person or small group of people. It is less clear how ethical responsibility attaches to specific actions for which the outcome is very distant and unclear, or in cases where the individual’s motives and intentions are overwhelmed by larger interactive effects. (p. 53)

In an increasingly networked global context, individual actions and choices link up with the economic and political situations of others the world over via supply chains, waste production, big data, and so on. The acknowledgment of such dilemmas is not new, being exemplified in boycott movements of the past and present. Technologies perpetuate global interconnection, as the speeds and regularities in which people communicate increases through new affordances for interaction and engagement. Yet normative microethical analyses tend to become more problematic when trying to confront and address large-scale problems such as climate change or electronic waste. While such issues may appear tangential to the ethics of teaching and learning, schools and their employees are nonetheless implicated in them when deciding to upgrade their schools’ computer labs, for example.

Unlike the microethical focus on the proximate consequences of individual actions and interpersonal relationships, macroethics are meant to be analyzed through applied ethical frameworks for making judgments about large-scale issues (see Table 1). Cox (2013) defined macroethics as “a systematic understanding of moral responsibility on a global scale” (p. 29). It is the job of the macroethicist and those teaching macroethics to “describe, clarify and challenge our evolving notions of personal integrity and responsibility in the context of our times” (Cox, 2013, p. 29), that is, a sociotechnical global context of nation-states and the market societies that they coconstitute.

Table 1.

Selected Microethical and Macroethical Issues for Teachers and Technologists.

	Teaching and learning	Research and development
Microethics	Fair grading practices	Sharing results
	Plagiarism	Fair attribution
Macroethics	Digital divide	Big data
	Electronic waste	Technological unemployment

Because of the large-scale consequences of technological devices and sociotechnical systems, the concept of macroethics has long been explored in the social studies of technology (e.g., Hudspith, 1991). Indeed, Herkert (2009) was explicit about the centrality of technology in 21st-century macroethics, which he defined as a profession’s commitment to “collective social responsibility and to societal decisions about technology [italics added]” (p. 437). The macroethical concerns of technologies have reached far across a variety of disciplines and areas, including nanotechnology (Vallero, 2007), military R&D (Allenby & Mattick, 2009), and many others. While much of this work includes discussions on teaching and learning, engineering education practitioners and researchers have been leaders in developing scholarship on macroethics research and pedagogy (e.g., Herkert, 2005).

Similar to teacher education, ethics discourses in engineering education tend to focus on microethical concerns. Yet the sociotechnical macroethical issues of engineering are well documented. This includes contributions to structural violence (Banks & Lachney, 2017) and perpetuating climate change (Herkert, 2009). The fact that engineers so obviously create and maintain technologies for the purposes of shaping the material world at both micro- and macrolevels has encouraged engineering professionals and professional societies to start taking collective environmental and social responsibility seriously, not only for those in their immediate proximity but around the globe. For example, Herkert (2009) made the case that since engineering projects have contributed heavily to climate change, one of the most pressing intergenerational issues of our time, they must learn to engage in public policy debates.

While there is still much work to be done in making macroethics as central as microethics to engineering professional codes and conduct, acknowledging the fact that the engineering professions are implicated in these macroethical issues creates a first step toward the design and implementation of pedagogical case studies and lesson materials on macroethics. Kline (2010) argued that designing case studies that bridge micro- and macroethics may help “rethink the field in regard to the interrelationship between engineering practice and technology policy” (p. 19). Indeed, case studies are particularly relevant since they put students into the role of evaluator and decision-maker. Warford (2016), for example, developed a case study around the Volkswagen diesel scandal to help engineering students bridge micro- and macroethics through exploring different ethical frameworks (e.g., justice, rights, etc.).

As these examples make clear, integrating macroethics into case studies may make professional practices appear more complex and less straightforward than microethical lenses offer. Bielefeldt (2016) noted that engineering students and faculty have mixed reactions when confronted by macroethics in the classroom since problems cannot always be solved through a single right or wrong answer. Confronting macroethics as a pedagogical problem, Riley (2008) developed an engineering-based approach to “liberative pedagogies,” where students work with the complexity of macroethical issues by engaging diverse worldviews from feminist and postcolonial theory to interrogate power relationships through self-direction, critical thinking, and reflective action. Therefore, it appears that to make macroethics relevant to the classroom requires rethinking not only what content should be included in coursework but also rethinking pedagogical practices to help students deal with the complexities that macroethical issues invoke.

Three Areas of Macroethical Concern

While engineering educators are well versed in macroethical debates and problems of technology, teacher educators are only beginning to address these types of issues. This article aims to follow the trajectory of engineering educators who identified macroethical issues, developed case studies and pedagogical practices, and brought them into course design for examination through different ethical lenses and frameworks. To lay a foundation for such developments that are specific to teacher education, three areas of macroethical concern at the intersection of education and technology are presented below.

The first, obsolescence, considers macroethical issues along the supply chain of computing technologies that might be found in schools. The second concern of automation introduces some of the ethical issues of integrating artificial intelligence and robotics into pedagogical practices. Finally, concerns surrounding big data introduce macroethical issues around the collection of students’ data by technology companies and the state. We show how these issues bring social, economic, and political life to bear on ethical analyses of technology. The introduction of these areas of concern is followed by a discussion about double binds, which aims to help case study and course designers anticipate teachers’ personal and interpersonal struggles when confronting macroethical issues that result in multiple discordant demands or undesirable choices.

Obsolescence

Given the plethora of choices on the market, schools face ongoing dilemmas about choosing what equipment to purchase for computer labs and supporting their technological infrastructures more generally: What will best help student-learning or connect to the district-mandated curriculum? What will stand-up to repetitive use by students over time? What will have staying power, lasting long enough until the school is ready to upgrade? What will help young people understand the role of technologies in supporting global connectivity, providing instant information at learners’ fingertips? Less at the forefront of these decisions is the fact that many of the technologies that are found in schools are part of a production model with global impacts on human health and the environment. At the intersection of these less considered issues is the concept of planned obsolescence, a blanket term for corporate strategies intended to generate a renewable market for goods. Put simply, planned obsolescence ensures a pattern of continual technology upgrades and incentivizes (or requires) consumers—including school administrators, teachers, and students—to buy the latest technology.

Consider, for example, that the typical lifespan of electronic devices in North America is 2 to 4 years (LeBel, 2016), with cell phones often being discarded after only 18 months (Slade, 2006). Part of this obsolescence is engineered, that is, producers intentionally design devices to stop working after a period of time, or purposefully limit their compatibility and usefulness. The other part is psychological—the result of marketing strategies that emphasize the value and allure of new things and encourage dissatisfaction with what folks already have (Slade, 2006). The strategies of planned obsolescence appear to be just as persuasive for school districts and teachers as they are for individual consumers. As more and more schools adopt 1:1 policies and the pace of technology development continues to accelerate, they are purchasing and discarding devices in increasingly larger quantities. It is therefore worth examining these cascading results in the context of education.

The digital devices that power educational technologies are built from physical components (many of which are highly toxic) that are often extracted from the environment in damaging ways, assembled by workers who are exposed to harmful health effects, and transported by container ships that release pollutants as they cross the seas to deliver the final products—at which point they are used for a brief period of time and ultimately discarded. The environmental and health impacts of the extensive supply chain that supports technology use are thus multifaceted and long-lasting. What is more, the pace of technology development and corporate practices of planned technological obsolescence threatens to accelerate those impacts on an alarming scale.

In addition to the health risks to workers who mine the toxic materials needed to supply the education and technology market, the tangible damage of planned obsolescence can be seen in the rapidly accelerating production of electronic waste (e-waste) as devices are continually replaced and discarded. E-waste is an extremely fast-growing waste stream industry, with a total volume in 2012 estimated to be over 1 billion kilograms (LeBel, 2016). This waste contains high levels of biological toxins. When devices are burned, toxic pollutants are released into the environment; when they are buried in a landfill, toxic pollutants seep into groundwater (Slade, 2006). Even when e-waste is disposed of properly, the harmful effects of the recycling process disproportionately affect poorer communities, where fewer governmental regulations and lower costs facilitate the proliferation of e-waste recycling facilities. The discourse of “recycling” tends to obscure the unsustainable global model of extraction, production, transportation, and disposal. Even when e-waste is recycled, it still has a significant negative impact on health and the environment. In sum, the accelerating pace of e-waste production due to planned obsolescence does not give ecological systems time to recover (LeBel, 2016).

Because of lax work safety and environmental regulations in many countries that import e-waste, workers (some aged younger than 15 years) may use unsafe methods to disassemble old electronics—for example, cracking cathode ray tubes and melting plastics—thus, exposing themselves to toxic chemicals (Agyei-Mensah & Oteng-Ababio, 2012). The effects on human health can be seen in case studies of facilities near Agbogbloshie in Accra, Ghana, where cases of acute respiratory infection rose by 59.6% between 2006 and 2008, after e-waste activities increased substantially in the area (Agyei-Mensah & Oteng-Ababio, 2012).

Complicating the issue of e-waste in this region is the layering of multiple historical and structural injustices that support the conditions for ecological and socioeconomic injustice in Ghana (Akese & Little, 2018). Examined through a critical, pluralistic framing of environmental justice (Schlosberg, 2013), current conditions in Ghana emerge not only from the environmentally racist practices of wealthier countries (and corporations) but also from the long history of exploitation and less visible “slow violence” (Nixon, 2011) that stem from land disputes and legacies of colonialism (Akese & Little, 2018). Further, e-waste facilities provide employment opportunities to support crucial and immediate needs of e-waste workers and their families. In other words, the spaces most affected by e-waste are in the midst of socioecological change, and are tied to specific historical, cultural, economic, and political contexts where persistent socioeconomic injustices facing workers may outweigh health and environmental concerns (Akese & Little, 2018; Burrell, 2012). Considered from an environmental justice perspective, technology supply chains should seek to balance basic human needs with a sustainable ecological system—but just as important, the broader injustices that create the conditions for continued exploitation must also be addressed.

With these understandings, educators and key stakeholders in educational technology may thus find themselves simultaneously making both desirable and undesirable choices. On the one hand, purchasing decisions and upgrade strategies must take planned obsolescence into account to ensure that students and teachers have access to functional digital devices that will support learning. On the other hand, as global citizens with an interest in environmental and health justice, responsible educators must also reflect on the lasting negative environmental and health effects that these decisions have on a worldwide scale as well as the socioeconomic and racial injustices that sustain the exploitation of marginalized communities. At the very least, this dilemma may present an opportunity to explore the complex macroethical implications of technological obsolescence and the roles that educational technology manufacturers and consumers play in perpetuating its negative consequences.

Automation

Automation is generally understood as the replacement of human tasks, jobs, and careers by machines, robots, and other technological systems. While automation is certainly not a new phenomenon—just consider the historical Luddite movement—it has taken on new relevance in the 21st century with the exponential growth of computing power. Indeed, a contentious 2013 study estimates that 47% of U.S. jobs are at risk of automation (Frey & Osborne, 2013). Given the increased coupling of education and technology in the 21st century, it is worth looking at these risks in the context of U.S. education.

Take, for example, how massive open online courses (commonly referred to as MOOCs), distance learning programs, and online instruction can be produced and used repeatedly to deliver content, reducing the need for local design and development. Or consider how, in collaboration with the multinational educational publishing company Pearson, IBM’s artificial intelligence system, Watson, is being designed to support personalized instruction with the use of machine learning and cloud computing in classrooms (IBM & Pearson, 2016). At more mundane levels, automation can be seen in attendance systems, electronic gradebooks, assessment programs, and other tasks that underpin curriculum and instruction. What does this mean for teachers and their colleagues? To begin answering this question, consider a piece of fiction from the late 1990s.

In 1998, philosopher of technology Langdon Winner (2011) appeared in a short, self-written film titled The Automatic Professor Machine—a spoof on automatic teller machine or ATM—as his alter ego, L.C. Winner, CEO of Educational Smart Hardware Alma Mater, Inc. or Edu-Sham. In this short comedy, Winner positions the audience to take seriously a foundational question: “What is education?” L.C. Winner defines education in instrumental terms as the “transfer of knowledge from point A to point B through the most efficient, low-cost link possible” (Winner, 2011). With this definition in mind, he went on to profess that students will no longer need to be burdened by inflexible face-to-face classes, centralized campuses, or human professors because of Edu-Sham’s latest innovation, the automatic professor machine or APM. Tongue in cheek through and through, Winner’s short film warns viewers about the profit-motive of those who aim to automate education by poking fun at the rhetoric that technologists and entrepreneurs use to sell their latest devices and programs.

If, and how, teaching may be programmed and re-created in automated environments is a question that has been asked in numerous ways since the cognitive revolution of the 1950s, and has been modeled by programmed sequences (Leinhardt & Steele, 2005) and curriculum scripts (Putnam, 1987). In contrast, more recent work posits that teaching is a profession with specialized knowledge that is hard to automate. Frameworks such as Technological Pedagogical Content Knowledge (Mishra & Koehler, 2006) support the idea that teacher knowledge is specialized and unique. Indeed, Selwyn (2017) explains how “teaching roles were generally calculated to be at a relatively low risk of automation. Elementary school teachers were ranked as the #20 least likely job out of 702 to be automated” (p. 123). Still, Winner’s warning is warranted. Selwyn (2017) went on to note that more at risk are “post-secondary teachers (#112) and teaching assistants (#317)” (p. 123).

Ford (2015) described how technologies that automate can be seen by teachers as labor-saving devices, but that “when the algorithms begin to encroach on an area believed to be highly dependent on human skill and judgment, however, many teachers see the technology as a threat” (p. 129). Teachers must, therefore, balance labor saving with the very real threats of deprofessionalization from prepackaged content (online and offline) and the potential technological unemployment that some of their colleagues (e.g., paraprofessionals, teaching assistants, etc.) may face. As jobs in schools are reshaped, reduced, and/or eliminated through automation, teachers and their unions may be able to provide support to those who are in more precarious situations than themselves. While their colleagues are of immediate concern, considerations of automation should reach beyond schools. For members of students’ families who work in industries where automation is likely to result in unemployment (e.g., truck drivers, restaurant employees, etc.), what is seen as technological progress by some may increase poverty for others.

Even if automation creates more jobs in the long term than it eliminates at a macroscale, at a microscale automation may lead to the conditions of unemployment or deskilling. The anxiety that surrounds automation may correlate with specific political attitudes. In asking the question “[w]as the outcome of the 2016 U.S. Presidential Election shaped by a growing automation anxiety?” Frey et al. (2017) found evidence for a positive relationship between communities’ risk of or exposure to automation and voters’ support for Donald Trump (p. 1). Could it be that those who economically lose out to automation are more likely to support radical political change? The answer remains unclear, but it helps us think and ask questions about the relationship between increases in automation and a community’s socioeconomic status, some of which are relevant to education.

Given correlations between the socioeconomic statuses of communities and students’ academic outcomes (Anyon, 2014), we might speculate and hypothesize that in the short term unemployment or deskilling due to automation in communities that schools serve may have a negative consequence on students’ achievement. Although teachers do not have full control over the technologies which enter their classrooms, let alone those of the industries that are located in students’ communities, they should use a critical lens and leverage the power of their own positionalities to evaluate the technologies employed in their learning spaces and the communities they serve. They should pay specific attention to how these technologies will impact their own labor, the labor of their colleagues, and that of their students’ friends and families.

Big Data

Since 1974, there have been federal guidelines in place to protect student privacy through the Family Educational Rights and Privacy Act (FERPA), which mandates that schools need written permission to release information to others, such as potential employers. The United States Department of Education even provides training modules for stakeholders to improve and maintain their knowledge of FERPA; however, laws and local policies have not kept up with the proliferation of digital technologies, increased use of social media, current trends toward ubiquitous computing, and the availability of big data, all of which add new dimensions to issues of privacy and surveillance. A recent analysis of the use of cloud computing in K-12 schools, Reidenberg et al. (2013) found that while 95% of districts use cloud services for educational purposes such as data mining for student performance and classroom activities, they do not have adequate expertise for governing how those data are protected. The authors found that only 25% of the schools inform parents of the use of cloud services,

Fewer than 25% of the agreements specify the purpose for disclosures of student information, fewer than 7% of the contracts restrict the sale or marketing of student information by vendors, and many agreements allow vendors to change the terms without notice. (Reidenberg et al., 2013, para 10)

In addition, vendors for cloud services may keep students’ data in perpetuity in ways that are outside of localized control. Take for example, how K-12 students create and share their work with peers and teachers using cloud services like Google Classroom and Google Docs, which they can transition into a Google account once they graduate from high school. Although Google provides schools access to free services, there is often little thought given to the implications of those services on student privacy or how their data become part of larger aggregates. Given that Google’s business model is largely based on tracking and collecting vast amounts of data from online searches and activities (Zuboff, 2019), this potentially gives Google access to aggregates of young people’s interests and behaviors, in and out of school, starting at an early age.

The “behavioral surplus” that is created from users’ interactions with Google services has enabled the rise of a particular economic logic that Zuboff (2019) calls surveillance capitalism. While industrial capitalism relies on the extraction of natural resources to be processed by its means of production (i.e., human laborers, technologies, etc.), surveillance capitalism extracts human experiences from individuals’ digital interactions with search algorithms, social media, and so on. This “surplus” is then claimed by big technology companies like Google and Facebook as “raw-material supplies and targeted for rendering into behavioral data” (Zuboff, 2019, p. 19). Like and dislike industrial surplus-value—the difference between the total value a worker creates and the wages they receive—that is taken for company or corporate profit, the surplus of information from individuals’ digital activities is gathered and then aggregated and processed through machine learning algorithms to be used to help make predictions that can aid in the intervention and shaping of consumer behaviors.

Goals to predict and shape teaching and learning have underpinned many educational reforms and initiatives in the United States, from the early 20th-century applications of Taylorism in the classroom to 21st-century value-added modeling. Therefore, the coupling of surveillance capitalism with education is not surprising. Companies like Pearson and McGraw-Hill Education have invested in machine learning and artificial intelligence with the hopes of one day being able to make predictions about students’ learning in the near (and, perhaps, far) future. As Pearson vice president of “advanced computing and data science,” John Behrens explained,

Before the digital world, if you wanted data on students, you had to stop the student, instrument or test them, then go on your way. In a digital ocean, you don’t have to stop that instruction, there is an interplay. The data is emerging naturally through homework, through games and play. (Johnson, 2018, para 4)

The notion that data are emerging “naturally” from a “digital ocean” of school activities speaks to its potential in creating behavioral surplus, ready for extraction by education and technology companies. The main problem with this type of logic is that it “ignores the key point that the essence of the exploitation here is the rendering of our lives as behavioral data for the sake of others’ improved control of us” (Zuboff, 2019, p. 94).

In terms of saving time and personalized learning there may be some real benefits to the application of machine learning in education (see Luckin, 2018). However, when these algorithms and technologies are subsumed by surveillance capitalist logics there is a risk that behavioral surplus will be extracted for the ends of profit—textbook sales, prioritizing the visibility of certain websites over others, or selling behavioral data to noneducational entities—with little concern for the localized conditions of students and teachers. Unfortunately, many algorithms that are part of institutional decision-making do not easily lend themselves to individual or group self-determination. One only needs to look at the controversy surrounding Cambridge Analytica during the 2016 U.S. presidential election to see how voters’ behaviors may be targeted, if not outright modified, for purposes other than their own (e.g., political campaigns).

Building on this last point, the use of large aggregates, from online behaviors or existing institutional data, can reproduce socially entrenched forms of racial and gender discrimination, reinforcing harmful stereotypes in the process. Building on the claim that “artifacts have politics” (Winner, 1980), Noble (2018) introduced the concept of “technological redlining” to show how algorithms embed discrimination into computer programs to create new types of racial profiling (p. 1). For example, it is well established that the U.S. criminal justice system is biased against communities of color and has resulted in mass incarceration that relies on the containment and controlling of Black bodies (Alexander, 2012). Within this context, Larson et al. (2016) analyzed an algorithm from Northpointe, Inc.’s commercial tool COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) that, with the help of existing criminal justice data, is used by judges and parole officers to predict criminal defendants’ likelihoods of recidivism. They found that the tool clearly reproduced racial biases, including how “Black defendants were twice as likely as white defendants to be misclassified as a higher risk of violent recidivism, and white recidivists were misclassified as low risk 63.2 percent more often than black defendants” (Larson et al., 2016, para. 73). Understanding how algorithms and data (re)produce structural racism has implications for teachers’ professional development on macroethics.

In efforts to disrupt the school-to-prison pipeline through teacher education and professional development on racial literacies (e.g., Sealey-Ruiz, 2011), for example, concepts such as technological redlining can be shared with educators to help them understand how macroethical issues surrounding big data and the politics of algorithms intersect with race and racism. This can help teachers be vigilant and critical in their own assessment of the algorithmic predictions that may be made about their students and the communities their schools serve. What is more, efforts should be made to help teachers be part of the democratic steering of technology and data use in their schools. If the algorithms of artificial intelligence and machine learning are going to be used in education to the benefit of local teachers, students, and communities the challenge will be to enable their data—through legislation and grassroots activism—to be part of “reinvestment cycles” (Zuboff, 2019) that keep the value of behavioral surplus in the hands of those who created it in the first place, not extracted for corporate or state ends (Eglash et al., 2019).

Embracing the Double Bind of Macroethics in Teacher Education

Macroethical issues are complex and large in scale by definition. Introducing them to teachers can add layers of complexity to the already complex situations that they face day-to-day in their schools. Indeed, one can imagine a teacher who struggles to come up with computationally rich lessons, learns a given set of tools, and finally secures a computer lab for implementation, becoming frustrated when asked to consider macroethical concerns surrounding the production, distribution, and waste of computers that are largely beyond their control. This is to say that asking teachers to take macroethical issues seriously may put them in a situation where they feel contradictory demands are being made or that whatever choice they make results in negative outcomes (a recipe for apathy). Boycotting new computers because they were made in unethical ways disadvantages students; using them helps students but reinforces larger systems of health and environmental injustice.

Such dilemmas, with contradictory demands or multiple negative outcomes, can be described with the concept of the double bind. The concept is commonly attributed to cybernetician Bateson (1972), specifically his work on schizophrenia in relation to interpersonal family dynamics and communication patterns. Bateson was interested in the way that schizophrenia might be the result of learned behaviors from family interactions where children are placed in ongoing situations where one or more adults are making contradictory demands of them. For example, consider the request “You must disobey me.” As Fortun (2001) explained, “To obey the statement is to disobey it; to disobey it requires obeyance” (p. 12). Bateson hypothesized that the result of being in double binds continuously is a confusion of thinking that leads to behaviors commonly associated with schizophrenia. While Bateson’s initial hypothesis was empirically controversial and is outdated, as a theory of communication, beyond its association with issues of mental health, the concept has been used to make sense of social and interpersonal contradictions in disciplines that include psychology, anthropology, design, among many others.

Within these disciplines, double binds are not just hard choices but situations with multiple obligations, all of which are valued but ultimately inconsistent with each other. For example, design studies scholar, Nieusma (2004) explored how designers who might want to use alternative design processes (e.g., ecological design, feminist design, to name a couple) may face double binds when confronted by the durability and resilience of dominant social, economic, and political structures that their discipline is ill-equipped to change. In such cases, there are no clear ways to overcome the double bind. Instead choices and actions can be negotiated by identifying the existence of the double bind and working within that space through ongoing self-reflection and, sometimes, collective action.

Helping teachers identify and accept the existence of double binds that come up when confronting macroethical challenges should be central to integrating these issues into case studies and course content. Of course, changing the logics of surveillance capitalism to something that is more generative for local communities cannot be done by one individual. Similar to what Nieusma (2004) described with designers, the double bind that results from macroethical considerations at the intersections of technology and education may make teachers feel stuck or helpless. Therefore, two strategies can help teachers confront macroethical issues at the local levels of their classrooms, schools, and districts: (1) critical reflection and (2) collective action.

Reflection has been a key idea and process in teacher education since Dewey’s (2012) famous 1916 book Democracy and Education, where he argued that reflection means accepting responsibility for “future consequences which flow from present action” (p. 157). Yost et al. (2000) built on Dewey’s and other similar definitions of reflection in ways that frame criticality as a central component. Accordingly, critical reflection involves engaging “the assumptions underlying a decision or act and on the broader ethical, moral, political, and historical implications behind the decision or act” (Yost et al., 2000, p. 41). The goal of a critical reflective process, then, is cognitive change or, to put it another way, consciousness raising. Helping teachers interrogate and work through assumptions behind technology use may help them situate their actions within broader contexts, inside and outside their schools, to alleviate some of the stress or discomfort that macroethical double binds may create. Yet critical reflection should not stop short of identifying opportunities for action.

Yost et al. (2000) explained that critical reflection should include problem-solving components that identify goals for improvement and action plans for accomplishing these goals. Given that macroethical issues are difficult to confront at an individual level, plans should be framed as collective action. Consider, for example, how in early 2018 West Virginia teachers challenged the logics of surveillance capitalism. Their public employee health insurance plan included a “workplace wellness program” that used an app called Go365 (Gaffney, 2018). As part of this program, teachers were rewarded for downloading the app and connecting it to a wearable tracking device (e.g., FitBit) that would monitor and record their biometric data. They were financially penalized for refusing. The insurance provider could profit off the surplus of their normal, everyday activities. No one teacher could have resisted if they wanted insurance, but after a collective outcry Republican Gov. Jim Justice announced that Go365 would be completely voluntary and the financial penalties were removed (Gaffney, 2018). This story helps us understand how collective action can be a means for negotiating double binds that teachers may find themselves in when confronting macroethical dilemmas.

Conclusion

This article has introduced the concept of macroethics and outlined three areas where it can be brought to bear on the use of technology in U.S. education. Unlike microethical issues in which the consequences of local actions are directly experienced and felt, macroethical issues take place at a larger scale, often outside of individual control or choice. Yet, this does not make them any less important to consider through ethical frameworks in teacher education. The issues of obsolescence, automation, and big data can certainly be framed by microethics but only doing so misses the larger sociotechnical contexts in which individual actions are implicated.

There are many other areas of macroethical concern at the intersections of technology and education that have not been highlighted here. These three can act as a guide for what the identification and analysis of macroethics may look like for teacher educators. Indeed, obsolescence, automation, and big data were chosen as examples because they provide clear contexts where teachers actions—whether it is upgrading their computer lab or using an automated system that replaces tasks of paraprofessionals—can be linked up to a larger global context in which technologies have become the impetus for economic and social connections. It is our hope that this article will lead to the integration of macroethics into case studies and course content. However, doing so can make teachers feel as if they are in a double bind, possibly resulting in unintended consequences in terms of in-service teachers’ attitudes about these issues and themselves. Therefore, it is crucial to emphasize how critical reflection and collective action are strategies for helping teachers understand and negotiate macroethics.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Michael Lachney

Author Biographies

Michael Lachney is an assistant professor of Educational Technology at Michigan State University. He has a background in science and technology studies and qualitative social science methods. Michael’s research explores the cultural politics of educational technology design and implementation.

Liz O. Boltz earned her PhD in Educational Psychology & Educational Technology from Michigan State University, where she studied the ways videogames can promote transdisciplinary habits of mind. She is also an assistant professor in MSU’s Master of Arts in Educational Technology program.

Brittany Dillman is a doctoral candidate in the Educational Psychology and Educational Technology Program at Michigan State University. She is also the Graduate Certificate Programs Director for the Master of Arts in Educational Technology Program at MSU. Broadly, Brittany is interested in teacher thinking, lesson planning, and classroom discourse in mathematics education.

Candace Robertson is a program coordinator for the Master of Arts in Educational Technology program at Michigan State University. Her work revolves around supporting educators in developing meaningful and creative experiences for their learners. She utilizes technology to facilitate conversation about pedagogical approaches across disciplines and grade levels.

Aman Yadav is a professor of Educational Psychology and Educational Technology at Michigan State University with extensive experience in research, evaluation, and teacher professional development. His research and teaching focus on improving student experiences and outcomes in computer science and engineering at the K-16 level.

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