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Classrooms as Laboratories of Democracy for Social Transformation: The Role of Data Literacy



In this chapter, we re-envision classrooms as “laboratories of democracy” (Guinier & Torres, 2002), or spaces of democratic participation where students purposefully use and repurpose quantitative literacies as they engage in the complexities of collective deliberation. We first outline our rationale for shifting from an individualistic lens that tends to highlight innumeracy to a collective lens that allows researchers and educators to better notice and build on situated numeracy. We then elaborate Guinier and Torres’ (2002) notion of “laboratories of democracy” through a brief discussion of “power-with democracy.” We use these constructs to critically re-examine our prior efforts to incorporate data literacy into classrooms (see Footnote 2 regarding our use of data literacy rather than quantitative literacy). To add nuance to our vision of classrooms as laboratories of democracy, we contextualize the potential role of data literacy in these spaces by exploring the risks of prioritizing data literacy over democratic deliberation. We therefore argue for making space for “laboratories of democracy” within classrooms, where students can learn data literacy as they practice “power-with” democracy.
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Classrooms as Laboratories of Democracy for Social Transformation:
The Role of Data Literacy
Thomas M. Philip, University of California Los Angeles
Laurie Rubel1, Brooklyn College
Toward a Collective Vision of Numeracy
Steen’s (1997) influential paper emphasized the potential for quantitative literacy to empower
people as individuals. Through improved numeracy, Steen argued, people can become thrifty
consumers, productive and employable workers, informed voters, and enthusiasts who more
deeply enjoy recreational and cultural activities. Underlying this orientation toward the
individual, however, are assumptions that our political and economic systems are neutral and
fair, that there is equitable access to numeracy, and that empowering individuals through
quantitative literacy will aggregate to an improved society.
The assumptions that our political and economic systems are impartial can be challenged on a
host of fronts. For example, there is persistent racialized residential segregation in the U.S. that
results in inequitable geographies of opportunity that impact consequential domains like
healthcare, education, personal finance, transportation, and food (Hogrebe & Tate, 2012).
Racism in the United States also gives rise to unjust policing and criminal justice systems that
result in the disproportionate mass incarceration of African Americans (Alexander, 2010). There
is unyielding sexism that fuels violence against women and produces a gender wage gap in
which women earn about 80% of men’s earnings, a gap which is most pronounced for African
American and Latina women (Hegewisch & DuMonthier, 2016). Classist economic policies have
led to drastic inequalities where a mere 20 Americans have more financial assets than the bottom
half of the country—157 million people—combined (Collins & Hoxie, 2015). The assumption
that increased numeracy in individuals will ameliorate these inequalities suggests that their lack
of quantitative literacy is a root cause. Such an attribution ignores the underlying systems that
position a few in domination of the many. The presumption that increased numeracy amongst
individuals will lead to social justice further burdens those who are subjugated, rather than
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challenging and changing the unjust hierarchies on which the injustices rest. Imperative but
absent here is an outline as to how people collectively adopt, adapt, transform, and judiciously
select and reject quantitative literacies toward a vision of a more equitable and just society.
In this chapter, we re-envision classrooms as “laboratories of democracy” (Guinier & Torres,
2002), or spaces of democratic participation where students purposefully use and repurpose
quantitative literacies as they engage in the complexities of collective deliberation. We first
outline our rationale for shifting from an individualistic lens that tends to highlight innumeracy
to a collective lens that allows researchers and educators to better notice and build on situated
numeracy. We then elaborate Guinier and Torres’ (2002) notion of “laboratories of democracy”
through a brief discussion of “power-with democracy.” We use these constructs to critically re-
examine our prior efforts to incorporate data literacy into classrooms (see Footnote 2 regarding
our use of data literacy rather than quantitative literacy). To add nuance to our vision of
classrooms as laboratories of democracy, we contextualize the potential role of data literacy in
these spaces by exploring the risks of prioritizing data literacy over democratic deliberation. We
therefore argue for making space for “laboratories of democracy” within classrooms, where
students can learn data literacy as they practice “power-with” democracy.
Rethinking our Starting Point: Noticing Numeracy rather than Displaying Innumeracy
The choice of whether to use an individualistic or collective lens influences how researchers and
educators see, study, and address numeracy. Scholarship that focuses on the proficiency of
individuals has documented extensive innumeracy across the populace, even among those
successful in school mathematics and statistics (Lusardi & Wallace, 2013; Madison, 2004;
Paulos, 1988). Similarly, examples of individuals’ lack of proficiency with respect to decision-
making about civic issues abound (Carnevale & Desrochers, 2003; Cohen, 2003; Orrill, 2001;
Wilkins, 2000). However, anthropological research that studies people as they interact with
others in everyday activities have demonstrated that people use sophisticated quantitative literacy
practices in these contexts (Lave, 1988; Saxe, 1990). Building on this lens that focuses on people
in collectives, we argue that studying more genuine forms of democratic interaction will allow us
to better understand how people authentically use quantitative literacy as they collectively
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engage with civic and political questions (see Boyler & Roth, 2006; Philip & Azevedo, in press
for similar arguments about scientific literacy).
Social transformation is clearly more complex than learning to apply quantitative reasoning to
civic and political problems. Conventional quantitative practices, which often gloss over the
historical, social, political, and economic dimensions of power, will not be sufficient to ignite or
facilitate authentic transformations. Rather, social transformation necessitates new ways of
thinking, new tools, and new forms of quantitative literacies. In the words of Audre Lorde
(1984), “The master’s tools will never dismantle the master’s house. They may allow us
temporarily to beat him at his own game, but they will never enable us to bring about genuine
change.” Approaches to change sanctioned by existing systems of power, where dominant
interests converge (Bell, 1980), may allow for certain temporary gains for oppressed and
marginalized groups, but they will not fundamentally reorganize the inequitable and unjust
systems themselves.
Within the constraints of contemporary schooling, it is difficult for classrooms to become
genuine spaces of social transformation. But, they can certainly approximate these spaces and
foster democratic deliberation (Hess, 2009; Hess & McAvoy, 2015). Toward this end, we argue
that classrooms should not only be spaces where students master existing quantitative literacies
that they can later apply as civic agents. Instead, classrooms should be “laboratories of
democracy,” where students purposefully use, repurpose, and innovate quantitative literacies as
they participate in “power-with” democracy (Guinier & Torres, 2002).
Power-with Democracy
We draw on Guinier and Torres’ (2002) conceptualization of democracy to further consider the
relationship between quantitative literacy and social transformation, a relationship in which
power plays an essential role. Power as seen in the dominant paradigm is “power-over;” that is,
competition for power yields the strong who emerge as victorious and dominate the weak. From
this perspective, power is zero-sum, meaning that one person’s acquisition of power represents
another’s loss. Guiding the “power-over” paradigm is a self-perpetuating narrative that “justifies
both the winners and the rules by which they win” (Guinier & Torres, 2002, p110). From the
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perspective of a “power-over” paradigm, it is sensible to construct quantitative literacy as an
avenue towards individual empowerment, as a means to bolster an individual’s strength in a
competition for power. However, we accept Guinier and Torres’ proposition that political
transformation demands more than individual empowerment, meaning that the underlying
“power-over” paradigm needs to be challenged.
As an alternative to the “power-over” paradigm, Guinier and Torres (2002) propose that we
consider “power-with,” which is relational, interactive, participatory. From the “power-with”
perspective, a focus on the individual is replaced with an emphasis on resistance and struggle in
collective forms. Guinier and Torres (2002, pp. 152-153) explain, “By rehearsing their capacity
to resist oppressive forms of power-over, participants imagine alternatives to their own moral or
material distress […]. Change occurs under the radar: people participate, deliberate, and
emotionally engage. Not only do the low become high or the powerful weak but ideas of power
and hierarchy themselves become destabilized.” A fundamental difference in the goal of
empowerment in a “power-with” framework is to “change asymmetrical power relationships,
rather than merely struggle for the right to participate in them” (Guinier & Torres, 2002, p147).
Laboratories of Democracy
“Laboratories of democracy” are essential building blocks for the realization of “power-with”
democracy (Guinier & Torres, 2002). Laboratories of democracy are places where people come
together to experiment with ideas, share information, and reflect and self-correct. These spaces
must entail a commitment to attend to internal and external forms of power with respect to “the
dynamics of group interaction” and “the content of inquiry” (Guinier & Torres, 2002, p. 148).
Members attend to how their privilege and positionality influence their participation and the
assumptions and sensemaking they gravitate toward. Attending to norms and practices of
engagement are crucial through which “differences in perspectives are examined out in the open
to develop greater insight, stimulate constructive disagreement, and spark innovation” (p. 148).
A focus on internal processes of deliberation emphasizes the importance of how we engage in
democratic deliberation, not simply the goal of coming to an “informed decision” or even
achieving a shared goal. Guinier and Torres (2002) illustrate the significance of process through
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the example of Septima Clark’s citizenship schools. While the guiding objective was to increase
the number of Black Southerners who could exercise their vote, the schools did not focus solely
on voting. Rather, the schools gave Black Southerners the opportunity to meet, tell their stories,
and speak their minds. “Voting alone could never substitute for the process of formulating,
articulating, or pursuing a citizen-oriented, community-based agenda.” (p. 149). Through this
example, Guinier and Torres (2002) echo Lorde’s (1984) insights about the constraints of
societally-sanctioned tools for change: a temporary gain in an objective such as voting rights is
limited if it is not accompanied by more fundamental changes in asymmetrical relationships of
Guinier and Torres (2002) acknowledge that practices that are genuinely committed to a power-
with participatory framework may appear messy and unstable and can be incredibly time-
consuming. But, they are indispensable for genuine democracy. By confronting “embedded
internal hierarchies” and engaging “the untidiness of conflict over time,” laboratories of
democracy allow people to “struggle against external challenges in ways they have not yet
imagined [while they still] struggle with internal conflict” (Guinier & Torres, 2002, p. 159). In
doing so, laboratories of democracy introduces “new possibilities for reciprocity, collaboration,
problem-solving, networking, and innovation” and allows “communities to learn what it does not
know and what it has difficulty learning” (Guinier & Torres, 2002, p. 159).
Learning from Two Cases of Data Literacy in Classrooms
We reflect on the successes and struggles we encountered in two projects in which we
incorporated data literacy in classrooms with the explicit goal of addressing issues of democratic
engagement, equity, and social justice. We build on these experiences to envision classrooms
where students can learn and use data literacy in collective inquiries modeled after laboratories
of democracy.
Case I:
Over the last six years, Philip researched a large-scale high school reform project intended to
introduce students to the increasingly influential field of data science. The year-long curriculum
was developed by university-based researchers in statistics, computer science, and education in
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collaboration with school district personnel. Interspersed through the year-long curriculum were
numerous opportunities for students to engage in data literacy. For instance, students were
introduced to the ways in which corporations use big data to predict consumer behaviors for
marketing. They learned about highly publicized cases like Target’s ability to use data analytics
to predict pregnancies (Duhigg, 2012) and seemingly more innocuous examples such as using
data from Facebook posts to find patterns in seasons when people are more likely to end
romantic relationships. Students also collected, interpreted, and analyzed self-generated data so
that they might develop more personal and nuanced understandings of data at each of these
The focus on connecting data to contemporary issues and personal interests seeded powerful
opportunities to engage in the civic dimension of data literacy. The curriculum encouraged
students to develop the statistical and computational dimensions of working with data. However,
socially significant questions arising from the data were often glossed over since they could not,
ostensibly, be answered with the data or analysis tools that were available. The apparent fidelity
to statistical thinking and the desire to engage students in personally and civically relevant issues
consistently brought issues of race and racism to the fore without adequate lenses to examine
systemic and institutional forms of racism. Without a commitment to address internal hierarchies
and external forms of power, these potential opportunities instead became instances where deficit
perspectives about students and about communities of color were reproduced (Philip, Schuler-
Brown, & Way, 2013; Philip, Way, et al., 2013).
The introduction of a seemingly benign data visualization about movie rental patterns was a
particularly notable example of when a cursory approach to race backfired (Philip, Olivares-
Pasillas, & Rocha, 2016). The curriculum developers included a data visualization of a popular
blockbuster movie and a niche film with an all African African cast to illustrate how some
visualizations can simply be “proxies” for race. The classroom discussion, however, became a
heated and extensive contestation over what it meant to be African American. The students and
teachers in the class reproduced both internal hierarchies and external forms of power as some
students drew on the visualization to associate African American neighborhoods with ghettos.
The voices of African American students in the class were marginalized and alienated and all the
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students lost out on powerful opportunities to learn about data literacy and practice democratic
deliberation. In a second example, an inquiry into students’ snacking habits re-inscribed ideas
that low-income people of color eat unhealthy snacks because of poor choices they make (Philip,
Rocha, & Olivares-Pasillas, in press). The curricular insistence to make statistically sound
arguments based on the limited data students had collected about their snacking habits precluded
analyses of systemic factors pertaining to differential access to healthy foods based on
geography, income, transportation, work schedules, etc. However, there was a great deal of
latitude when teacher and students made generalizations about people of color based on trends
they saw in the data they had collected. There were not similar expectations for students to make
judicious arguments based in social theory when they made assertions that had to do with race.
The demand for students to think like a data scientist (in a relatively narrow sense), but not
necessarily like a social theorist, gave the air of scientific objectivity to problematic claims about
the nutritional habits of people of color.
Philip’s reflections about this project demonstrate that teaching data literacy with the assumption
that students will then better understand issues that are at the intersection of race and racism,
personal agency, and the distribution of resources in society is naïve at best, and perhaps more
accurately, negligent and deleterious. When addressing power in a highly racialized society in
the course of teaching data literacy, it is essential that opportunities are made for students to
become racially data literate – racially literate about data and data literate about race (Philip,
Olivares-Pasillas, & Rocha, 2016). While closer attention to issues of race can undoubtedly
address some of the shortcomings Philip documented, classrooms as laboratories of democracy
offer an even more genuine space of dialogue to develop and nuance racial data literacy.
Case II:
As part of an effort to design and study teaching mathematics for spatial justice, Rubel, Hall-
Wieckert, and Lim (2016a) describe two curricular modules to enable investigations about the
state lottery (Local Lotto) and about a city’s two-tiered personal finance system (Cash City). The
modules were designed by an interdisciplinary team of educational researchers with disciplinary
commitments to mathematic, urban planners with expertise in mapping, and classroom teachers.
In both modules, high school students used mathematics to understand the underlying mechanics
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of each system; probability and combinatorics in Local Lotto to understand the expected value of
a lottery ticket and percent and ratio in Cash City to model loans. The spatial dimension of data,
pertaining to the geographic distribution of lottery ticket sales or the locations of types of
financial institutions afforded opportunities for students to use data literacy to think critically
about these systems as social issues. Different from the examples described in Case I,
mathematically-oriented arguments suggested by quantitative data were nuanced with multiple,
and at times, competing narratives gathered by students from families, community members, and
local businesses through participatory mapping (Rubel, Lim, Hall-Wieckert, & Katz, 2016b).
We believe that spatial justice curricula, like Local Lotto and Cash City, are promising as seeds
for laboratories of democracy in schools, and are especially conducive to interdisciplinary
investigations that promote data and other quantitative literacies. For example, Local Lotto
powerfully impacted many students, who used their probability calculations, new sense of
scaling, and new understanding of the lottery as a state fundraiser to try to sway their parents
towards minimal or no participation. Analyses of the lottery quickly opened up spaces to
consider people’s motivations for playing the lottery that would have been otherwise missed if
the lottery was viewed as a matter of individual choice. For instance, students considered the
tensions low-income people might experience in having to choose between their “hunger for
food” and their “hunger for hope,” noting that lottery tickets and food are sold by the same stores
(Rubel, Lim, Hall-Wieckert, & Sullivan, 2016c). Participation in Local Lotto enabled students to
reflect on how “low-income people become inherently more vulnerable to the lottery because of
the hopelessness connected to the day-to-day realities of living on a low income” (Rubel et al.,
2016c, p.19).
Along with students’ mathematical progress and success in analyzing these systems as predatory,
other students instead emphasized the value of individual freedoms, to choose to buy lottery
tickets or to make use of alternative financial institutions like check-cashers and pawnshops
instead of banks. Rubel et al. (2016a) reflect how this resistance from students might be a
reflection of libertarian or capitalistic values but also could represent resistance to the way that
the modules’ narratives, led by White teachers and a White and Asian design team, might have
unintentionally positioned students and their families as innumerate and powerless, instead of
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positioning these systems as unjust and how they might be reimagined to be fairer. Issues related
to internal hierarchies of power in those classrooms were exacerbated by the omission of race
from the associated data visualizations. The lottery and the personal finance system as systems
are directly related to money and seemed most connected to data related to income and income
inequality, but by focusing on income and omitting race, the visualizations not only left race
implicit but also egregiously aggregated all of the city’s diverse low-income neighborhoods into
a single category. As in Case I, race was persistently raised by students (i.e. “the White man” in
response to teacher posed question “who’s making money off of this system?”), but was quickly
rejected by other students (“you can’t say that, there are poor White people too”) or, was too
volatile or risky to be picked up by this teacher (Rubel et al., 2016a). Here too, race was not
sufficiently attended to and deficit perspectives about residents of low-income communities were
likely reproduced.
More broadly, Local Lotto and Cash City could have been ideal precursors into a classroom
space as laboratory of democracy, in which students examine and address deeper issues of hope
and hopelessness relative to themes like collective responsibility, community health, and
taxation. To echo Guinier and Torres (2002), the real issue for a vibrant democracy is not
specific to any one industry or system, but rather, how deliberation about the equity of state
sponsored systems can support students to understand and address collective aspirations and
desperations. Essential to this deliberation as part of a laboratory of democracy is for students to
reimagine systems so to achieve greater justice.
The Risks of Prioritizing Data Literacy over Democratic Deliberation
There is certainly value in the data literacy activities Philip, Rubel, and their colleagues
attempted to incorporate into high-school classrooms. These approaches were opportunities for
students to see instances in which data literacy might be relevant to everyday problems and to
experience a range of ways in which data literacy can contribute to “reading and writing the
world” (Freire & Macedo, 1997; Gutstein, 2006). The more we engage with data literacy for
democratic participation, however, we become concerned that classroom data literacy activities
like these do not go far enough to create spaces where students can practice “power-with”
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democracy. The projects we describe reach their limits precisely when they could have opened
up spaces for classrooms to become laboratories of democracy.
Classrooms as laboratories of democracy allow students to adopt, adapt, transform and
judiciously select and reject quantitative or data literacies within the multiple, intersecting, and
often conflicting intricacies of social existence. Deep social struggles are not problems that can
be solved with the help of equations, data visualizations, or computational simulations alone;
they are intricate problems that involve people as complex actors. In the above cases, we have
demonstrated how data literacy can contribute to the process of democratic deliberation. At the
same time, we caution against overemphasizing the role of data literacy and below highlight
several examples that explore the risks of an over-emphasis on data literacy in the process of
democratic deliberation. This list is by no means exhaustive; it is only meant to show how a
singular focus on data literacy can have deleterious effects on democratic deliberation.
1. When data literacy is “powerless”
Statistics about policing policies such as “stop and frisk” in New York City and the killings of
young African American men by police across the United States clearly show systemic racism
(Gelman, Fagan, & Kiss, 2012; Tolliver, Hadden, Snowden, & Brown-Manning, 2016). Even
when movements have sought to bring racism to light by leveraging data literacy practices, the
populace, particularly White Americans, often dismiss statistically sound reasoning about
systemic racism and displace the responsibility on allegedly wayward youth of color (Kahn &
Martin, 2016). It is naïve and disingenuous to assume that fostering data literacy for individuals
about stop and frisk and police killings is sufficient to transform the extant policing practices.
Students must learn to analyze issues like policing practices, supported with a lens of data
literacy, but framed in the context of systemic racism. Students must practice engaging
themselves and others in addressing complicity with the systemic racism that leads to these
unjust policing practices. By highlighting social transformation towards justice as their goal,
classrooms as laboratories of democracy would engage new uses and transformations of data
2. When “real” people choose to disregard data literacy in the name of other ways of knowing
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Cash City, one of the curriculum examples described in Rubel et al. (2016a), pit alternative
financial institutions (AFIs) like pawnshops and check-cashers against banks for comparison. By
virtue of their higher interest rates and density in certain spaces, the AFIs can quickly seem
predatory and choosing AFIs over banks would seem to be contraindicated by these quantitative
comparisons. And yet, during their data gathering through participatory mapping, several African
American students experienced exclusion and racism in local banks and felt more welcomed in
AFI spaces (Rubel et al., 2016b). Pedestrians who were interviewed by students shared narratives
that yielded additional complexities, such as the role of banks in gentrification and its impact on
community life. These contrasts between the financial institutions were not otherwise captured in
the quantitative datasets but add significant nuances to more accurately illuminate people’s
decision-making as other than innumerate. We posit that a valuing of a diversity of ways of
knowing would better support authentic reimaginings of unfair systems. A classroom as a
laboratory for democracy would allow students to engage in more realistic explorations of
complex systems like these, which take into account factors broader than those suggested by data
3. When a narrow focus on data literacy excludes other perspectives
A focus on data literacy can detrimentally marginalize other important perspectives. For
instance, in the class Philip studied, students analyzed self-generated data about their snacking
habits. However, given the data they had collected, there was little room to contextualize their
data within their larger sociopolitical context. Students largely explained their “unhealthy”
snacking habits through a deficit-oriented lens (Philip et al., in press) that focused on poor
choices made by students, their parents, or their communities. The curriculum did not support an
inquiry into how lenses of immigration, urban food deserts, and colonialism could have
complicated the simplistic and problematic data-based argument that students produced.
Classrooms as laboratories of democracy could provide a space for students to genuinely
consider and address food inequities and injustices.
4. When our positionality allows us to see different things through the lens of data literacy
Coal plants have become a contentious political issue. Steeped in data-based arguments,
environmentalists have called for their closure; unions, on the other hand, have resisted the loss
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of well-paying, union represented jobs that the plant closures would lead to (McAlevy, 2012).
Both arguments might be sound from a data literacy perspective, but their resolution would
require individuals to put their perspectives, as environmentalists and union laborers, in dialogue
with each other in ways that address internal and external forms of power. Every region faces
parallel tensions between different interests and goals. Conventional approaches to data literacy
will most likely lead to prioritizing one perspective over the other, or at best, an effort to address
one goal while mitigating the impact on the other. Classrooms as laboratories of democracy
could provide a space where “differences in perspectives are examined out in the open to
develop greater insight, stimulate constructive disagreement, and spark innovation” (p. 148).
A focus on data literacy, as we illustrate above, risks oversimplifying the historical, social,
political, and economic dimensions of highly textured and complex problems in an imperfect
democracy. Prioritizing data literacy in classrooms over the processes of democratic deliberation
can effectively undermine democracy. There is most definitely the need for spaces and times
when classrooms focus on data literacy skills and practices. And classrooms need not always be
laboratories of democracy. But, if there is a genuine commitment to nurturing students’ use of
data literacy toward civic and political engagement, there must be opportunities for students to
practice power-with democracy as they adopt, adapt, transform, and come to terms with the
limits of data literacy.
The good intentions of creating opportunities for students to learn data literacy with the
expectation that they will apply it to civic and political problems that they encounter in everyday
life is unrealistic and perhaps even detrimental. It begins with and reproduces the assumption
that individuals’ data illiteracy is a root cause of their powerlessness. We have shown how
conventional approaches that prioritize data literacy over democratic deliberation are not
reflective of the intricacies of the real world and ultimately erode the liberating possibilities of
“power-with democracy” (Guinier & Torres, 2002). We have argued for a radically different
approach to data literacy that carves out spaces in classrooms for students to practice “power-
with democracy.” Through a critical reflection on our prior work that attempted to incorporate
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data literacy into classrooms, we outlined the potential for classrooms to become “laboratories of
democracy” where data literacy is part of a collective movement toward transformation and
social justice. With transformation and social justice as a priority, data literacy can support
students as they learn to “change asymmetrical power relationships [both internal and external],
rather than merely struggle for the right to participate in them” (Guinier & Torres, 2002, p. 147).
Such an approach to data literacy would open new avenues for social transformation, new
possibilities for collectivism and mutual reciprocity between participants, and at the same time,
encourage the development of new data literacies as tools that better support this vision.
1. The authors equally contributed to conceptualization and writing of this chapter.
2. In the remainder of the chapter, we use the term data literacy in preference to quantitative
literacy. New digital technologies and the ubiquity of digital data—pervasive today in
ways that were unimaginable for Steen (1997) and his collaborators just a decade and a
half ago—have positioned data literacy and quantitative literacy as mutually
indispensable. With the preponderance of new forms of digital data, people are required
to be quantitatively literate about data that are more messy, incomplete, and unstructured
than in the past. A current framework for quantitative literacy must account for the
drastically changed and inescapable contexts of data and data literacy. Data literacy, as
we conceive it, has its origins in quantitative literacy and shares many mathematical and
statistical practices but also incorporates practices from other disciplines (e.g. data
representation and visualization practices common in computer science and mapping and
spatial literacies from geography).
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... As such, scholars of data science and data visualization across disciplines (e.g. Boyd and Crawford 2012;Hullman and Diakopoulos 2011;Irgens et al. 2020;Philip, Olivares-Pasillas, and Rocha 2016;Philip and Rubel 2019;Philip, Schuler-Brown, and Way 2013) have argued for more critical approaches to data literacy education. They note that datafrom generation and collection, through representation and visualization -are never neutral and objective (Hullman and Diakopoulos 2011;Irgens et al. 2020;Gillborn, Warmington, and Demack 2018). ...
... To analyze the data in this study, we mapped these five CRT concepts onto data literacy scholars' recommendations for more critical approaches to data visualizations in order to develop a framework for analysis of social studies materials. We began with the broad, overlapping recommendations from both social-justice oriented data literacy scholarship and state social studies standards (Boyd and Crawford 2012;Hullman and Diakopoulos 2011;Irgens et al. 2020;Philip, Olivares-Pasillas, and Rocha 2016;Philip and Rubel 2019;Philip, Schuler-Brown, and Way 2013; for what students should do with data -that is, that students should learn to comprehend data, use data, and create data. From there, we read across the social justice-oriented data literacy scholarship, as well as QuantCrit scholarship that rejects the idea of objectivity in statistics (e.g. ...
Data visualizations, including timelines, maps, and graphs, are often used to present social and political information in the media. Students need to learn how to make sense of data visualizations and recognize when they are being used to mislead, or when they advance white supremacist views. In this study, we used critical race analysis to examine history lessons from four widely-used websites in order to determine if they provided opportunities for students to acquire critical data literacy skills and understand how they presented race and racism in data visualizations. Findings reveal that many lessons miss opportunities to include data visualizations, and, when included, they offer little guidance for helping students understand them. In addition, data visualizations rarely spotlight race or racism, thereby making racial issues and the experiences of marginalized people invisible. We offer advice for teaching data literacy in social studies that centers race and racism.
... A data-driven world implies the essentiality of data practices (We prefer data practices to "data literacy," because we recognize there is a history of racialized and discriminatory uses of the term "literacy" (Philip & Rubel, 2019) or to data skills, because we view learning data science as a process of participation in situated practices with tools (Gutiérrez & Rogoff, 2003).). Accordingly, an enthusiasm for data science (DS) education-that is, education that leverages computer science, statistics, and mathematics knowledge for learning data practicesnow extends to K-12 schools (e.g., LaMar & Boaler, 2021). ...
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In this paper, we introduce Notice, Wonder, Feel, Act, and Reimagine (NWFAR) to promote social justice in data science (DS) education. NWFAR draws on intersectional feminist DS to scaffold critical perspectives towards systems of power and oppression and attend to students' experiences in designs for learning. NWFAR adds three practices that are typically not emphasized in learning designs for DS: feel-engaging emotions and the physical body; act-challenging, inspiring, or informing others towards change; and reimagine-envisioning how data, data methods, and data technologies could pursue different problems, solutions, and perspectives. We illustrate NWFAR through two design-based research projects from prior empirical work. Through these two examples, we demonstrate what thinking with NWFAR could look like in practice and highlight future possibilities for learning. We conclude with a discussion that focuses on the reimagining dimension, in which we highlight social-justice oriented theories.
... The ability to leverage existing data of all forms is increasingly recognized as a skill that is needed in virtually every discipline (Wise, 2020). As a result, efforts to incorporate data literacy and analytics throughout K-16 education are on the rise (Jiang & Kahn, 2020;Lee & Wilkerson, 2018;Philip & Rubel, 2019). In this paper, we argue that a neglected area of data literacy is dealing with unstructured data as well as the restructuring of data to prepare for effective modelling. ...
To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models. While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data. There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making. Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models. Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies. Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources. To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
... There are other mainstream mathematics educators who invoke democracy in other interesting ways. See Philip and Rubel's (2019) Classrooms as Laboratories of Democracy: The Role of New Quantitative Literacies for Social Transformation. In it they add cooperation and community to their socially conscious application of data literacy. ...
In the spring of 2018, the Pomona College mathematics department hosted a community seminar on identity, culture, and power in the discipline and education of mathematics. The seminar was free and open to all students, faculty, and staff of the college. In this paper I describe the specifics of the seminar, what types of issues we discussed, what a typical seminar session looked like, and what we all gained from the experience. Similar community seminars might support mathematics departments and faculty in their own endeavors to create inclusive and healthy mathematical communities.
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In this reflective essay, Laurie H. Rubel, Maren Hall-Wieckert, and Vivian Y. Lim present a design heuristic for teaching mathematics for spatial justice (TMSpJ) based on their development of two curricular modules, one about the state lottery and the other about financial services in a city. Spatial tools, including data visualizations on maps and participatory mapping, were designed for youth to examine spatial injustices in these systems. The authors' findings report reflections about supporting students to "read and write the world with mathematics" (Freire & Macedo, 1987; Gutstein, 2003). These reflections inform an expanded design heuristic for TMSpJ.
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Data visualizations are now commonplace in the public media. The ability to interpret and create such visualizations, as a form of data literacy, is increasingly important for democratic participation. Yet, the cross-disciplinary knowledge and skills needed to produce and use data visualizations and to develop data literacy are not fluidly integrated into traditional K–12 subject areas. In this article, we nuance and complicate the push for data literacy in STEM reform efforts targeting youth of color. We explore a curricular reform project that integrated explicit attention to issues pertaining to the collection, analysis, interpretation, representation, visualization, and communication of data in an introductory computer science class. While the study of data in this unit emphasized viewing and approaching data in context, neither the teacher nor the students were supported in negotiating the racialized context of data that emerged in classroom discussions. To better understand these dynamics, we detail the construct of racial literacy and develop an interpretative framework of racial-ideological micro-contestations. Through an in-depth analysis of a classroom interaction using this framework, we explore how contestations about race can emerge when data visualizations from the public media are incorporated into STEM learning precisely because the contexts of data are often racialized. We argue that access to learning about data visualization, without a deep interrogation of race and power, can be counterproductive and that efforts to develop authentic data literacy require the concomitant development of racial literacy.
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This article explores integrating place-based education with critical mathematics toward teaching mathematics for spatial justice. Local Lotto, a curricular module with associated digital tools, was designed to investigate the lottery as a critical spatial phenomenon and piloted in urban high schools. This article describes findings from the second iteration in a remedial class in a low-income neighborhood. The research questions consider how the spatial focus supported the learning of mathematics and provided opportunities for students to think critically about the lottery using that mathematics. Findings include student interest in and engagement with the theme of the lottery familiar from outside of school with associated social justice implications. Students used mathematics and spatial evidence, at various levels of spatial scale, to support arguments about the lottery with greater success at narrower levels of scale. Suggestions about further innovations to scaffold place in a “critical pedagogy of place” in mathematics are provided
The academic frames of diversity and, to a lesser extent, intersectionality mute the roles played by race and racism in the recent police killings of unarmed Black people in the United States. Most scholars agree that racism today is systemic, and the authors of this article assert that diversity content fails to prepare social workers to situate client issues in a social work practice that incorporates an understanding of the impact of racialization on Black people seeking social work services. Race and racism in the United States are creations of White supremacy and were initially crafted to justify the enslavement of Africans. The ideologies are a part of the founding principles of the U.S. democracy, and they are the genesis of White advantage in the United States achieved through law and social policy and still enacted daily in human interaction. In this article, the authors place police killings of unarmed Black people in a context that takes into consideration historical and contemporary evidence of the denigration of Black people in the United States. Then we specifically challenge the utility of diversity as a frame to prepare social work practitioners for practice with Blacks whose ancestral lineage traces back to people of African descent and whose lived experiences in the United States occurred during slavery and/or legally sanctioned apartheid (Jim Crow).
This book seeks to analyze the issue of race in America after the election of Barack Obama. For the author, the U.S. criminal justice system functions can act as a contemporary system of racial control, even as it adheres to the principle of color blindness.