ArticlePDF Available

Notice, Wonder, Feel, Act, and Reimagine as a Path Toward Social Justice in Data Science Education



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.
Kahn, J. B., Peralta, L. M., Rubel, L. H., Lim, V. Y., Jiang, S., & Herbel-Eisenmann, B. (2022). Notice, Wonder, Feel, Act,
and Reimagine as a Path Toward Social Justice in Data Science Education. Educational Technology & Society, 25 (4). [Early
ISSN 1436-4522 (online) and 1176-3647 (print). This article of the journal of Educational Technology & Society is available under Creative Commons CC-BY-NC-ND
3.0 license ( For further queries, please contact Journal Editors at
Notice, Wonder, Feel, Act, and Reimagine as a Path Toward Social Justice
in Data Science Education
Jennifer B. Kahn1*, Lee Melvin Peralta2, Laurie H. Rubel3, Vivian Y. Lim4, Shiyan
Jiang5 and Beth Herbel-Eisenmann2
1University of Miami, USA // 2Michigan State University, USA // 3University of Haifa, Israel // 4CUNY Guttman
Community College, USA // 5North Carolina State University, USA // // // // // //
*Corresponding author
ABSTRACT: 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; actchallenging, inspiring, or informing others towards change; and
reimagineenvisioning 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.
Keywords: Data science education, Data feminism, Critical data literacies, Social justice
1. Introduction
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) educationthat is,
education that leverages computer science, statistics, and mathematics knowledge for learning data practices
now extends to K-12 schools (e.g., LaMar & Boaler, 2021). The preK-12 Guidelines for Assessment and
Instruction in Statistics Education (GAISE II; Bargagliotti et al., 2020), for example, present statistical literacy
for all as an ultimate goal (p. 5). However, stated goals like statistical literacy for all promote equity but
with a generality that conceals structural inequities embedded within data practices that harm people from
historically marginalized groups (Benjamin, 2019; Martin, 2003). Expanding access to data practices does not in
and of itself respond to systemic inequities like racism or how the use of data might be a driver of injustice and
oppression (Benjamin, 2019; Eubanks, 2018; Noble, 2018; ONeil, 2016; Philip et al., 2013). Furthermore, the
contemporary expansion of K-12 education to include DS, in terms of resources, technologies, and programs,
rarely extends to education about how data and its uses could inspire or inform needed social change.
In this paper, we put forward a set of guiding questions to shape the design of learning environments for DS,
focusing on learning environments that support students creating and/or interpreting data visualizations to inform
decision-making and conclusions about real-world phenomena, with the goal of promoting social justice. We use
Swalwells (2013) conceptualization of social justice as one that:
[R]ecognizes and affirms difference (e.g., cultural, sexual, political) while maintaining a commitment to
fundamental human rights and democratic principles (e.g., freedom of speech and freedom of
religion)...[C]hallenges the current distribution of resources in order to secure the basic needs required for human
flourishing (e.g., safety, food, shelter, water, love)...[A]ssumes unequal power relationships and challenges the
belief systems and social relations that (re)produce power differentials (p. 17, citing Nussbaum, 1992 and Fraser,
Social justice is an ongoing process, rather than an outcome, that must be continually sustained to attend to both
material and ideological conditions that are always in flux. Further, we emphasize that this conceptualization of
social justice does not locate sources of injustice within the minds and actions of biased individuals. Instead, a
social justice orientation allows us to identify and challenge unequal power structures located in society at
multiple scales.
We first discuss two current approaches to centering social justice in learning designs for DS. We then introduce
and situate an instructional heuristicNotice, Wonder, Feel, Act, and Reimagine (NWFAR; for earlier
discussions of these ideas, see Rubel, 2020; Rubel et al., 2021a; Rubel et al., 2022)in response to these
approaches. We extend the Notice and Wonder instructional routine that originated with Burton (1984) by
drawing on intersectional feminist perspectives on DS (DIgnazio & Klein, 2020; Rubel et al., 2022). We then
use NWFAR to reexamine two examples from previous empirical work from this authorship team. Finally, we
point to scholarship outside of DS and statistics that offer new ideas for researchers and practitioners to work
towards social justice goals.
2. Beyond humanistic and critical approaches to DS education
Existing guidelines for engaging students with data tend to emphasize technical skills and, beyond a few passing
references to ethics, do not address its social uses and impacts. For example, GAISE II encourages educators to
open opportunities for students to pose a question and then collect, interpret, and represent data towards
answering their question (Bargagliotti et al., 2020). However, an approach to DS education that merely
encompasses technical skills leaves hardly any room for ethical and political considerations (Bhargava et al.,
2020), disregards the need to address deeper structural issues of inequality (Fotopoulou, 2021, p. 1641), and
does not help students critique data, data practices, or the contexts in which data carry meaning (Philip et al.,
2016). In the absence of adequate ethical and political considerations, individuals and institutions will continue
to use data to support practices of oppression through technologies such as facial recognition software
(Benjamin, 2019; Buolamwini & Gebru, 2018). For these reasons, it is imperative that educators promote the
technical aspects of data practices alongside knowledge of and a desire to act with respect to issues of social
justice and broader sociopolitical and cultural concerns related to data and data practices (Fotopoulou, 2021; Lee
et al., 2021; Philip et al., 2016; Wilkerson & Polman, 2020; Stornaiuolo, 2020).
Two interrelated strands of research engage data practices with understandings of broader sociopolitical, cultural,
and ethical concerns, the first of which takes a humanistic stance toward DS (Lee et al., 2021), while the
second promotes critical data literacies (Tygel & Kirsch, 2016; Stornaiuolo, 2020; Fotopoulou, 2021). Lee et
al. (2021) call for a humanistic stance that recognizes that data practices comprise intersecting personal, cultural,
and sociopolitical layers that shape learners experiences with data. They describe a humanistic stance as one that
attends to students personal and direct experiences with data, the cultural and sociotechnical infrastructures of
data collection and use, and broader political and social narratives that affect data practices (We understand Lee
et al.’s (2021) use of humanistic as different from “humanizing,such as in Paris & Winns (2013) humanizing
research, del Carmen Salazar’s (2013) humanizing pedagogy, or Gutiérrez’s (2018) rehumanizing mathematics,
in how the latter uses of the term emphasize decolonization.). Lee at al. (2021) propose this broad humanistic
stance as a starting place for more research and more specific frameworks that could support critical
participation in data practices (p. 7), including the technical practices (i.e., generation of data, interpretation,
argumentation) and considerations of how data is situated within systems of power and privilege (p. 4)
typically associated with critical data literacies.
The strand of research that promotes critical data literacies often draws inspiration from Freire (1993). Tygel and
Kirsch (2016), for example, frame critical data literacies in terms of reading, communicating, and producing
dataall toward emancipatory goals. Others add considerations of when and where data are collected, how data
are manipulated by hidden algorithms, and how ethics impact data practices, including concerns about privacy
and the skewed incentive structures created by data practices (DIgnazio & Bhargava, 2015; Hautea et al., 2017).
Philip et al. (2016) argue that data practices must occur alongside deep interrogations of race and power, without
which opportunities to learn about data can become counterproductive for failing to address connections between
data practices and white supremacy. Stornaiuolo (2020) suggests that key objectives of critical data literacies
include the opportunities for students to see themselves as agentic producers of data, to exploit data for their
personal purposes, and to understand that data are socially situated resources for meaning making (p. 81; see
also Pangrazio & Selwyn, 2019). Common themes across this body of work include the integration of technical
data practices with sociopolitical awareness of power and identity as well as an orientation towards
Common to both a humanistic stance toward DS and a critical data literacies approach is a focus on individual
students experiences with data, their agentic responses to data, and their capacities to intellectualize data and
data practices with personal, technical, cultural, and sociopolitical lenses. Consequently, these perspectives may
seem to overly emphasize the role of individual cognitive resources and rational responses to the way we
experience the world (Bondi, 2005). That is, these perspectives treat rational processes of human decision-
making and sensemaking as the primary objects of teaching and learning about data. However, as intersectional
feminist scholars (e.g., DIgnazio & Klein, 2020) and others interested in affect theories (e.g., Bondi, 2005)
argue, aspects of engaging with the world, including aspects of our engagements with data and data practices in
service of social justice, lie beyond the realm of rationalist explanation and sensemaking. These non-conscious
and non-individual processes include feelings, as well as affective dimensions of social action and imagination,
all of which are situated within relations of power among human and non-human individuals and collectives
(DIgnazio & Klein, 2020; Van Wijnendaele, 2011). Even justice-oriented researchers tend to privilege thinking
and discourse over emotional, felt, and non-conscious experience as the primary means of acting with respect to
social justice goals (Van Wijnendaele, 2011). In alignment with these critiques, we elevate feeling, action, and
reimagination with thinking and discourse as data practices important to the process of social justice. Our
proposed instructional heuristic, NWFAR, could be integrated into critical data literacies or humanistic
approaches to DS. On one hand, to support critical data literacies, NWFAR encourages participation in analytic
reasoning about data and its personal, cultural, and sociopolitical contexts. On the other hand, NWFAR also
prompts teachers and students to engage with data affectively, by positioning feelings, actions, and the
imagination as central aspects of DS education rather than peripheral ones, and to reflect on systems of power
3. Notice, Wonder, Feel, Act, and Reimagine (NWFAR)
NWFAR functions as a kind of reflection tool for thinking about designs for learning from an intersectional
feminist perspective. Such questions can inspire or influence new learning designs, for example, in the form of
new or revised curricular materials, approaches to teaching, or forms of assessment, that consider issues of
emotion, praxis, and the imagination, which are not captured by popular strategies such as the Notice and
Wonder routine in use (Burton, 1984; Fetter, 2015; Gonchar & Schulten, 2017; National Council of Teachers of
Mathematics, 2021). The Notice and Wonder routine begins when a teacher presents a data visualization and
asks students What do you notice? and What do you wonder? The routine has multiple purposes, including
facilitating participation, encouraging student sense-making, and communicating that there may be more than
one correct answer to a problem. GAISE II, for instance, uses the language of noticing and wondering to prompt
students to make observations about data (e.g., patterns or features of graphical displays, like axes or measures)
and ask questions about the data pipeline (e.g., how, why, and for whom data were collected; Bargagliotti et al.,
2020). NWFAR makes use of the instructional efficacy and popularity of Notice and Wonder in ways that
foreground social justice.
Figure 1. Notice, Wonder, Feel, Act, Reimagine
NWFAR revises the Notice and Wonder routine in two ways. First, NWFAR reworks the notions of notice and
wonder to be more closely tied to goals of social justice, rather than their original purposes of prompting
questions for mathematical sensemaking. Second, NWFAR creates opportunities for learners to feel, act, and
reimagine as responses to data and data practices, in addition to noticing and wondering. We draw on DIgnazio
and Kleins (2020) data feminism in developing these revisions. Data feminism is an intersectional feminist
perspective that offers an alternative to the male and white and technoheroic paradigm of DS (DIgnazio &
Klein, 2020, p. 9; see DIgnazio & Klein (2020) for examples and elaborated discussion; Lunn et al., 2021),
counters the prevalence of racism and sexism through DS, and calls for broader engagement of activists and
community organizers in DS (e.g., Data for Black Lives, n.d.).
Our proposal of NWFAR is not intended to be a routine in the way that Notice and Wonder has been adopted
among many mathematics educators, as a clearly defined ritual (see Rumack & Huinker, 2019). NWFAR is not a
step-by-step approach to prompt students to recognize what they notice, wonder, feel, act, and reimagine in a
particular order. Instead, it is a collection of possible starting points that can be approached and revisited in any
sequence (Figure 1). In the following sections, we briefly expand on each of the elements of NWFAR and
provide guiding questions for each dimension.
3.1. Notice
Notice refers to interpreting elements of the data visualization itself. These elements can include data sources,
data trends, relationships between variables, comparisons between data points, identification of outliers as well
as visual elements including titles, labels, annotations, or authorship information. Notice also refers to observing
what is absent or left out. Questions that promote practices related to noticing but often are overlooked in typical
curricular uses of notice include: (1) Whose perspectives are represented and whose are ignored? (2) How, when,
and where was the data produced and by whom? (3) How does the visualization account for race, gender, class,
and place and their intersection? (4) How do the authors describe the purpose of this visualization?
3.2. Wonder
Wonder means posing questions that are not directly answerable. Wonder raises questions about the data (and
data that are missing), the process of producing and communicating data, and the contexts in which data are
produced, for which there is no direct evidence in the data visualization. Questions that extend conventional
prompts of What do you wonder? include: (1) What might the data visualization look like if it (more
explicitly) acknowledged how race, gender, class, and place (and their intersections) play a role in the data
context? (2) Who might benefit from the represented point of view, and who might profit from this dataset? (3)
What might the data visualization look like if it more explicitly attended to human and non-human stakeholders
and their physical bodies? (4) What might be some of the assumptions that the designers of the data visualization
and activity make about people and relationships? (5) Who might have been involved in the collection,
production, interpretation, analysis, visualization, and communication of the visualization?
3.3. Feel
Feel represents practices of engaging with a learning activity or data visualization on a physical, emotional,
relational, and affective level. Embracing emotion represents an act of resistance against dominant practices in
DS that tend to code reason as masculine and emotion as feminine and thus elevate reason but cast suspicion on
emotion. Embracing emotion allows us to let go of the binary logic between reason and emotion and allow both
to inspire action toward justice (DIgnazio & Klein, 2020; e.g., hand-drawn data illustrations by Chalabi
(Rakotondravony, 2019)). Through an emphasis on feeling, we follow the affective turn in feminist theory to
challenge the notion of universal objectivity as a privileged goal when engaging with data (DIgnazio & Klein,
2020; Pedwell & Whitehead, 2012). Data practices can and should include visceral experiences that involve the
body, physically and emotionally (Malinverni & Pares, 2014). This includes engaging the body through senses
such as touch and the feelings that these senses generate (Lupton, 2017), through embodied practices such as
gestures and movement to interact with and represent data (Roberts & Lyons, 2019), and through emotions
(Kennedy & Hill, 2018). Questions include: (1) How does this data visualization engage your body, your senses,
and your emotions? (2) How might this visualization make space for multiple kinds of feelings? (3) How does
the data or context connect to interpersonal relationships or to relationships with nonhuman others and the
3.4. Act
Act refers to challenging unequal power structures and exposing inequalities through and within data practices.
DIgnazio and Klein (2020) offer examples for what acting can look like in DS: compiling counterdata,
challenging power by analyzing inequitable outcomes across different groups, working towards co-liberation,
and cultivating a new generation of data feminists. As we argued in earlier work (Rubel et al., 2021b), action
directed toward transforming systems of power must go beyond merely playing the game of DS. Instead, the
act dimension of NWFAR must also include seeking to change the game through critique and social action
(Rubel et al., 2021b; see Gutiérrez, 2011). Questions include: (1) How can you use the data or data visualization
to challenge and inform, inspire, or support political action? (2) How does this data visualization raise ethical
questions, including questions about data security and data privacy? (3) What might you learn from reaching out
to others about information presented in this data visualization? (4) In what ways are you moved to act, to refrain
from action, to make change, or to communicate to others?
3.5. Reimagine
Reimagine describes practices directed toward reworking DS tools, as well as the structures of the data context,
its uses, what counts as data, who counts as a data scientist, and who has sovereignty over data. For instance,
reimagining could entail thinking about how a data visualization could produce a different solution or show
different perspectives. This use of reimagination draws on the data feminism principle of elevating emotion and
embodiment in DS (DIgnazio & Klein, 2020; Ioannou & Ioannou, 2020) by challenging (e.g., artist Onuohas
(2016) Library of Missing Datasets calling attention to the absence of data) and reinventing existing data
visualizations and data stories. For example, the Data Zetu program in Tanzania created a fashion competition
and show in which fashion designers reimagined how open public health data could be used by designing
clothing for women that could spark conversations around sexual and reproductive health and sexual and gender-
based violence (Katuli, 2018).
Warren (2021) offers a way of thinking about reimagining, drawing on Kelleys (2002) Freedom Dreams: The
Black Radical Imagination. Warrens framework for centering possibility in Black education comprises nine
interlinked concepts: Resistance, Dreaming, Storytelling, Creativity, Thriving, Community, Reparations,
Environment, and Teaching. We expand on resistance (what needs to be torn down) and dreaming (what should
be built atop the rubble, p. 31) because of their importance in Warrens teachings about a future-oriented Black
education transformation and their potential for developing designs that make reimagining a more central aspect
of learning with data. Resistance encompasses multiple forms of opposition to education conditions, broadly
conceived, that insist on Black folks subordination to Eurocentric expectations for thinking, speaking, and
being (Warren, 2021, p. 21). Resistance requires critical reflection on how schooling, and, for purposes of this
paper, data and their contexts, necessitate opposition to unjust authority figures and systems. Dreaming provides
a means for reimagining the capabilities of Black (and other marginalized) individuals to produce novel
creations that respond to or solve a problem of interest to the maker: designs guided primarily by the desire of
the producer versus that of the colonizers (Warren, 2021, p. 36). Warrens articulation of resistance, dreaming,
and creativity link to the kinds of speculative fiction, science fiction storytelling, counter-storytelling, and
allegorical storytelling that have begun to appear in STEM education work. In the discussion following our
example analysis, we point to theoretical perspectives that offer more radical approaches to reimagining that
could move DS education further towards social justice and equity goals.
4. Returning to prior work to illustrate and extend NWFAR
In this section, we revisit prior work from among this authorship team using NWFAR. Both projects prompted
youth to explore questions about socioeconomic issues that resonated with their personal, family, or community
experiences through activities that included exploring georeferenced, large-scale datasets and digital data
visualization interfaces. The first example, the Lottery Project, was a design-based project, in collaboration with
a mathematics teacher, that engaged high school students in investigating the role and impact of the lottery on the
local community (Rubel et al., 2016a; Rubel et al., 2016b; Rubel et al., 2017). After learning how to calculate the
probability of winning various lottery games, the students collected data about peoples experiences with the
lottery and analyzed lottery spending across neighborhoods. The second example, the Family Migration Project,
comes from a design-based research study that asked teenage youth to investigate personal family migration
histories with data in a summer program (Kahn, 2020). Students explored family histories and experiences with
family members and connected those to analyses of socioeconomic data trends to tell the stories of what moved
their families.
We use NWFAR as a tool to analyze the intended and enacted designs. More specifically, we point to the
projects to demonstrate the feeling and acting components of NWFAR. We look across these two examples to
identify learning opportunities that could have been supported if one were to redesign with NWFAR in mind. We
show that our examples of reimagining are limited; when prompted, students reimagining stayed within existing
structures or systems. In our discussion, we point to possible directions for future iterations of NWFAR.
4.1. Feeling and acting in the Lottery Project
The Lottery Project is a curricular unit engaging high-school students to learn about the lottery as a public
system and its impact on their community (Rubel et al., 2016a; Rubel et al., 2016b; Rubel et al., 2017). Students
learn about the lottery by calculating the probability of winning local lottery games, analyzing ticket sales data
across their city, and noting how the state utilizes lottery revenue. They learn about the impact of the lottery in
their community by interviewing people about their lottery participation and mapping where lottery tickets are
sold. In retrospectively revisiting the learning designs for the Lottery Project using NWFAR, we identify
attempts in the design to engage students to feel by drawing on their own and their community members lived
experiences to inform critical analyses of the lottery system and prompt students to act and reimagine in
response. All descriptions below are based on field notes and artifacts from classroom observations conducted in
a high-school class in a large city in the Northeastern United States. Artifacts included photographs of student
work and of the classroom whiteboard.
Various activities in the Lottery Project prompted students to feel. For example, a lesson early in the unit asked
students to contribute their free associations with the lottery to create a collective Word Wall. The open prompt
engaged students personal reactions. Through the Word Wall activity, we learned that some students sensed that
the lottery is unfair, for example, with some expressing their chagrin that Black people dont win. These
collective ideas would factor into students stance toward questioning the lottery system. Through the project,
there were reflection opportunities, in which students could express other feelings. Examples of expressions of
feeling included surprise about the slim chances of winning and that this system is predesigned and owned by the
state; outrage about the lottery operating as a scam targeting low-income areas; resignation about the lottery
operating under the notion of free choice; and promise about the lottery providing hope and funding for
The Lottery Project included a field research component, in which students extended their inquiry into the
lottery’s presence and impact into the local neighborhood. This field research engaged participants physical
bodies by their interaction with people, places, and their physical environments. By interviewing people in the
neighborhood, students could supplement their own experiences and add complexity to the personal stories about
the relationships that people have to the lottery. For example, in a class debrief of their findings from these
interviews, students noted that some people buy lottery tickets out of an addiction, but many view the lottery as a
chance to satisfy basic needs and change their livespaying childrens college tuition, providing housing for
family members, or returning to their home countries. The field research enabled students to consider personal
and emotional elements in their understanding of the lottery alongside learning about the low probability of
winning these lottery games. They learned that playing the lottery is not necessarily indicative of poor
mathematical decision-making but rather an almost guaranteed outcome of an institution that is designed to prey
on the most vulnerable people.
The projects technology enabled multilayered analyses of qualitative information about peoples experiences in
context and quantitative data about lottery spending and revenues. Students explored interactive maps showing
daily lottery spending, median household income, and the ratio between these two figures by neighborhood. The
lessons guided students to make comparisons among neighborhoods and notice and wonder about how the ratio
of lottery spending to income is higher in lower-income neighborhoods. These interactive data visualizations,
created by the research team, were the base layers for embodied, qualitative data that the students gathered. That
is, students captured audio recordings, photos, and text from neighborhood interviews and uploaded these data as
geolocated objects. Thus, students connected and grounded their analyses of the city-scale quantitative data to
the local-scale and vice-versa. As a result, students had the opportunity to notice, wonder, and feel the lotterys
impact in a more nuanced way.
The project also created opportunities for acting and reimagining. For example, using combinatorics to calculate
the miniscule (yet hidden) probability of winning various state lottery games, students created posters to speak
back to the states Hey, You Never Know advertising campaign. Through these Hey, Now You Know
posters, students reimagined the public lottery messaging into educative forms. For example, a group of students
likened the chances of winning the Powerball game to finding a single, marked Skittle among 438,059 bags of
skittles (Figure 2). The students included a photo of a gleeful-looking woman posing with lottery tickets in hand,
captioned, Doesnt she look happy?!?! followed by a BUT and a list of caveats based on probability
calculations, concluding that Gloria wont be so happy when she realizes she is part of the large number of
losers. A second set of activities prompted students to reimagine the lottery as a system. As this played out,
students primarily considered how the lottery could be redesigned to produce more winners from their
community. Students were hesitant, despite guidance, to fundamentally reimagine the lottery system, as a source
of public funding, private income, state revenue, or as a way for people to change their lives. We interpret this
hesitation as shaped by how infrequently social justice is engaged in education as a matter of reimagining basic
systems that are indeed transformable.
Figure 2. An example of a reimagining of lottery advertising created by students
4.2. Feeling and acting in the Family Migration Project
A second project (Kahn, 2020) offers an example that moved beyond the traditional notice and wonder pedagogy
by centering family relationships and conversations as a starting point for data exploration. In this project,
students (n = 17, mostly African American, ages 11-16) participated in a free summer program at a public library
in a midsized southern city. Students connected their family migration histories (where they live, where they
moved to), or family geobiographies, to the socioeconomic push and pull factors that drive family migration
through an exploration of demographic datasets using two data visualization tools (Social Explorer, Gapminder).
All descriptions below were based on interaction analysis of video and audio records of activities, including
screen capture recordings of data exploration on laptops, as well as fieldnotes and students’ final PowerPoint
family data stories.
By starting with a retrospective, personal inquiry, we created spaces for the NWFAR dimensions of feeling and
acting by providing an opportunity for families to be involved in learning. The Family Migration project began
by asking students to trace their family geobiography back to their grandparents generation. The family
geobiography positioned the perspective of families at the center of the exploration of the aggregate data. During
the workshop, the project leveraged students’ relationships with siblings also participating in the workshop and
other family members. Students were encouraged to text or call parents or relatives during the sessions and talk
with their family on the intervening nights between workshop days. Family members consequently participated
in the assembling of data stories, and students worked to resolve and/or represent multiple voices and
stakeholders. This distributed effort (Kahn, 2020) challenged the traditional DS approach to human individuals
as isolated data points. Similarly, in informal interviews at the end of the project, nearly all students said they
would talk to family members to answer the questions that their projects raised. We view these conversations
with family members, both those that occurred in the workshop and those intended for the future, as related to
acting. The involvement of families and the telling of family histories also opened a space for feeling and
emotion while engaging with the data.
The design incorporated traditional approaches to notice and wonder around interpreting data displays. We asked
students to notice and wonder about aggregate-level trends in the data models and maps. However, we
consistently situated noticing and wondering practices in familial and personal contexts. For example, one of the
first activities asked students to brainstorm (wonder) about what moved families in general and then explore
those factors with variables using the data tools. Students looked for differences across places (where their
family member lived previously compared to where they moved to) on variables like income and education.
Variables were sometimes selected by students on their own, other times suggested by instructors. Once students
selected variables, instructors encouraged them to describe visual trends in the data. The interactivity of the data
visualization web-based tools and the breadth of available data sets supported both practices.
The personal nature of noticing and wondering supported enactments of feeling and acting. For example,
students frequently noticed that data that represented their family members experiences was unavailable or that
the data did not align with the story they wanted to tell. In turn, they pursued different strategies, such as the
inclusion of multiple data displays in their data stories, to resolve this trouble in the data modeling environments
(Jiang & Kahn, 2020). Steps to address the conflict could also be viewed as a form of acting with the data tools.
However, sometimes feelings clashed with noticing and wondering. For example, several students wanted to
pursue stories about why their family moved for which they could not find a related dataset. For instance, one
student wanted to tell the story of how her mother moved from Thailand to the United States for loveto marry
her father. Love, however, is a variable that does not exist in traditional demographic datasets. Another student
wanted to tell a data story about her familys celebration of Kwanzaa but similarly could not find an appropriate
demographic variable to represent the population that celebrates this holiday. In future iterations, these conflicts
between family stories and the limitation of datasets could be leveraged as opportunities for reimagining the data
and data visualization.
The project incorporated several embodied data activities as well that were intended to serve as resources for
reasoning about historical socioeconomic conditions from a relational perspective and with affect, although these
were outside of the data visualization environment (as opposed to the exploration of data displays driven by body
movements as in Roberts & Lyons, 2020). Students performed a walking-scale timeline that asked them to stand
in for the family in historical time; we repeated this embodied activity with parents and students at the
culminating public showing of student work. In all sessions, we also asked students to participate in a four walls
game (like a four corners game). When asking the students to select a family member, we were also asking them
to physically stand in and speak for their family members, who potentially had survived or witnessed
discrimination and prejudice. For instance, questions about occupations raised additional queries like what
constitutes work, particularly for students thinking of enslaved ancestors, with some students stating that any
forced labor under slavery should not qualify as an occupation. We also wanted students to be able to assemble
stories or models grounded in experiences of people of color and challenge dominant social ideologies of
equality and opportunity if they wanted to tell these stories. We found that students recognized their families
struggles in their data stories but focused on the positive outcomes for their family members. Being in charge of
their own stories, including what data to show, was powerful for students.
Finally, students were not asked to reimagine the data or data tools in this project. In this way, the use of existing
data visualization platforms and datasets arguably restricted the enactment of this part of NWFAR. However, our
design did have students’ futures in mind (e.g., we asked students where they see themselves living when they
grow up). Additionally, some students considered what their lives might have been like if their family had not
moved and assembled data stories around this reimagination of their own lives (a hypothetical counterfactual;
Kahn, 2020). Below we discuss other possibilities for reimagination.
5. Discussion
NWFAR draws on intersectional feminism to examine power and promote equity within DS. The values and
ethics embedded in this instructional heuristic depart from existing approaches to DS education. DS tends to be a
white male space that emphasizes individuality, competitiveness, and the development of technical and cognitive
skills but leaves out other ways of knowing and being. With NWFAR, we seek to add a new perspective to the
growing body of scholarship committed to countering the creation and perpetuation of harmful and
discriminatory data-driven practices that contribute to the marginalization of women, people of color, and other
historically marginalized groups.
Our reflections on two projects supported the development of NWFAR. The two projects are examples that
illustrate how learning designs can make space for feelings, both physically and emotionally, and motivate or
inspire action in ways that are productive for developing critical perspectives towards issues of power, equity,
and social justice. Both examples implemented notice and wonder in ways that attended to personal or local
community histories and experiences and emphasized feelings and emotions in data engagements. Students were
guided to act in various ways, such as creating posters as part of a public messaging campaign and having
conversations with family members about their geobiographical histories.
There were moments in these projects when students engaged in practices of reimagining, but broader
reworkings of the tools, structures, and uses of DS were not incorporated into the designs. Students were asked to
reimagine public messages around the lottery (e.g., Hey, Now You Know posters) or reimagine the lottery system
itself. In the family migration project, some explored what their lives would have been like if their families had a
different geobiographical story. There is ample room to deepen how we might engage students in reimagining. In
the Lottery Project, for example, we could have asked students to generate ideas for creating alternative
opportunities for investment that would directly benefit their own communities. More fundamentally, we could
have prompted students to consider what it would take for society to ensure that no one could be vulnerable to
relying on the lottery for basic needs and resources. In the family migration project, we could have asked
students to reimagine data that included their family members’ experiences or to develop their own alternative
history narratives, incorporating details about how their lives and the lives of their families and communities
might have shaped and been shaped by broader sociopolitical and historical forces. In this way, reimagining
becomes a way to use ideas about desirable futures and alternative pasts as tools to aid in speculative thought
about how life could or should be in the present. Thus, reimagining practices seek to center justice, dignity, and
freedom, especially for marginalized peoples and communities.
Additionally, our projects used public DS resources (open data visualization applications) as pedagogical tools to
foster students’ data practices. However, the use of professional data technologies in instructional settings raises
questions for how to design and use these resources to support critical data practices as well as relational,
affective learning experiences. In our project examples, to accomplish this, we intentionally connected activities
outside the data interfaces that generated qualitative data (e.g., brainstorms, field research, walking-scale
timelines) to data visualizations. Alternatively, there are other possibilities for achieving the goals of NWFAR
within the technologies themselves. For example, while new DSE research has looked at developing tools for
students to build machine learning models with data to gain a fundamental understanding of artificial intelligence
(AI; e.g., Biehler & Fleischer, 2021), the activity and AI interface could be designed to help students learn the
subjectivity of model development and decisions made by AI, and how AI can perpetuate biases and unfairness.
Both data technologies and the learning experiences created around them should be redesigned to support
complex reasoning about data to help students to engage with issues related to power, equity, and social justice.
5.1. Future directions: Developing reimagine in NWFAR
The act of reimagining—oneself, one’s community, and one’s society—is a powerful but often overlooked data
practice. As we have reflected, the projects could have more deeply engaged students in practices of reimagining
to better support social justice and equity. In line with how data feminism relies on broad and diverse examples
and intellectual traditions, we turn to literature outside of DS education, including Black feminist thought and
Indigenous quantitative methodologies. These theories draw on various frameworks and intellectual traditions,
including critical Afropessimism (as opposed to vulgar Afropessimism, see Woodson, 2021), radical hope
(Grant; 2021; Kelley, 2002), Black liberatory fantasy (Dumas & Ross, 2016), critical race theory (Crenshaw,
1991; Davis & Jett, 2019), BlackCrit (Dumas & Ross, 2016; Martin et al., 2019), Afrofuturisms (Alexander,
2019; Bell, 1992; McGee & White, 2021), Indigenous Futurisms (Dillon, 2012) and alternative histories (Carroll,
2020; Rick, 2021). The frameworks are necessarily interdisciplinary, allowing for new connections across
identities and disciplines (McGee & White, 2021).
Speculative education critiques historical and inequitable schooling conditions and seeks to actively reimagine
and create new possibilities for education (Mirra & Garcia, 2020). Mirra and Garcia (2020) worked with
classroom communities across the United States, asking students to define their civic communities, engage in
conversation with peers around a chosen civic topic, and imagine and represent potential civic futures. Their
third design cycle included opportunities for students to craft origin stories for their own civic superheroes and
explore how such superheroes could address issues they found to be pressing. Mirra and Garcia (2020) found
that the processes of imagination did not come easily to youth or adults as efforts of imagination came up against
the reality of clashing political ideologies among students. Thus, opportunities that include supporting students to
craft speculative narratives based on real-world datasets likely need attention to how practices of imagination
might confront entrenched political beliefs or dominant perspectives about the purposes and possibilities of data
and data technologies.
We emphasize that reimagining could become an empty gesture without careful consideration of the contexts in
which the research or design work is being done. We recognize that ideas about speculative education, Afro- and
Indigenous futurisms, and radical hope, among others, need to be thoughtfully and deeply applied to not
overlook oppressive histories and further damage or harm historically marginalized populations. Using these
theoretical perspectives requires serious consideration of whiteness, white supremacy, antiBlackness and
patriarchy while engaging with these liberatory ideas, a task that may be challenging as DS and technology
design fields continue to be dominated by white men.
6. Conclusion
NWFAR encourages DS education researchers and practitioners to raise important questions about data and data
visualizations and look for opportunities to explore emotion, praxis, and imagination. Our conceptualizations of
noticing, wondering, feeling, acting, and reimagining seek to go beyond mere participation in or access to DS
and move toward embracing new forms of engagement with data and data practices, including an emphasis on
affect and the relationship between data and issues of power. In this sense, NWFAR is a heuristic that directs DS
education towards social justice. The next steps are to implement and study NWFAR in various contexts,
including learning designs for K-12 education and teacher education. This process will generate information
about how to revise the heuristic and refine its set of reflective questions.
This material is based upon work done while Beth Herbel-Eisenmann is on assignment at the National Science
Foundation. This material is based on work supported by the National Science Foundation under Grant No.
1341882 and Grant No. DRL-1222430. Any opinions, findings, and conclusions or recommendations expressed
in this material are our own and do not necessarily reflect the views of the National Science Foundation.
This article is based on a conference proposal presented at the 2021 Annual Meeting of the American
Educational Research Association:
Peralta, L.M, Rubel, L., Herbel-Eisenmann, B., Lim, V., Kahn, J., Jiang, S. (April, 2021). Whats going on in
Whats going on in this graph? Research paper presented at the Annual Meeting of American Educational
Research Association, Online.
Alexander, N. N. (2019). Daija’s awakening: CRT and Afrofuturism in mathematics education. In J. Davis & C. C. Jett
(Eds.), Critical race theory in mathematics education (pp. 56-74). Routledge.
Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D. A. (2020). PreK-12 guidelines for
assessment and instruction in statistics education ii (GAISE II): A framework for statistics and DS education. NCTM & ASA.
Bhargava, R., Deahl, E., Letouzé, E., Noonan, A., Sangokoya, D., & Shoup, N. (2015). Beyond data literacy: Reinventing
community engagement and empowerment in the age of data. Data-Pop Alliance White Paper Series. Harvard Humanitarian
Initiative, MIT Lad and Overseas Development Institute Internews.
Bell, D. (1992). Faces at the bottom of the well: The permanence of racism. Basic Books.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press.
Biehler, R., & Fleischer, Y. (2021). Introducing students to machine learning with decision trees using CODAP and Jupyter
Notebooks. Teaching Statistics, 43, S133-S142.
Bondi, L. (2005). Making connections and thinking through emotions: Between geography and psychotherapy. Transactions
of the Institute of British Geographers, 30(4), 433-448.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification.
In S. A. Friedler & C. Wilson (Eds.), Proceedings of the 1st Conference on Fairness, Accountability and Transparency
(Proceedings of Machine Learning Research) (Vol. 81, pp. 77-91). PMLR.
Burton, L. (1984). Mathematical thinking: The Struggle for meaning. Journal for Research in Mathematics Education, 15(1),
Carroll, A. (2020). The Man in black: Alternate history where superheroes try to save the world [eBook edition].
Crenshaw, K. W. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color.
Stanford Law Review, 43, 1241-1299.
Data for Black Lives (n.d.). Data for Black Lives is a movement of activists, organizers, and scientists committed to the
mission of using data to create concrete and measurable change in the lives of Black people.
Davis, J. & Jett, C.C. (Eds.). (2019). Critical race theory in mathematics education. Routledge.
del Carmen Salazar, M. (2013). A Humanizing pedagogy: Reinventing the principles and practice of education as a journey
toward liberation. Review of Research in Education, 37(1), 121-148.
D’Ignazio, C. & Bhargava, R. (2015). Approaches to building big data literacy. Proceedings of the Bloomberg Data for Good
Exchange 2015. 07/Big_Data_Literacy.pdf
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT press.
Dillon, G. L. (Ed.). (2012). Walking the clouds: An Anthology of indigenous science fiction. University of Arizona Press.
Dumas, M. J., & Ross, K. M. (2016). Be real Black for me: Imagining BlackCrit in education. Urban Education, 51(4),
Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
Fetter, A. (2015, June 8). Ever wonder what they’d notice?: Annie Fetter [Video].
Fotopoulou, A. (2021). Conceptualising critical data literacies for civil society organisations: Agency, care, and social
responsibility. Information, Communication & Society, 24(11), 1640-1657.
Fraser, N. (1997). Justice interrupts: Critical reflections on the postsocialist condition. Routledge.
Freire, P. (1993). Pedagogy of the oppressed (Rev. ed.). Continuum.
Gonchar, M., & Schulten, K. (2017, September 6). Announcing a new monthly feature: What’s going on this graph? The New
York Times.
Grant, C. A. (2021). Radical hope, education and humanity. In C. A. Grant, A. N. Woodson, & M. J. Dumas (Eds.), The
future is Black: Afropessimism, fugitivity, and radical hope in education (pp. 65-71). Routledge.
Gutiérrez, K. D., & Rogoff, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational
Researcher, 32(5), 19-25.
Gutiérrez, R. (2011). Context matters: How should we conceptualize equity in mathematics education? In B. Herbel-
Eisenmann, J. Choppin, D. Wagner, & D. Pimm (Eds.), Equity in discourse for mathematics education: Theories, practices,
and policies (pp. 17-34). Springer.
Gutiérrez, R. (2018). Introduction: The Need to rehumanize mathematics. In I. M. Goffney & R. Gutiérrez (Eds.),
Rehumanizing mathematics for Black, Indigenous, and Latinx students. Annual Perspectives in Mathematics Education.
National Council of Teachers of Mathematics.
Hautea, S., Dasgupta, S., & Hill, B. M. (2017). Youth perspectives on critical data literacies. Proceedings of the 2017 CHI
Conference on Human Factors in Computing Systems (pp. 919-930).
Ioannou, M., & Ioannou, A. (2020). Technology-enhanced embodied learning. Educational Technology & Society, 23(3), 81-
Jiang, S., & Kahn, J. (2020). Data wrangling practices and collaborative interactions with aggregated data. International
Journal of Computer-Supported Collaborative Learning, 15(3), 257-281.
Kahn, J. (2020). Learning at the intersection of self and society: The Family geobiography as a context for data science
education. Journal of the Learning Sciences, 29(1), 57-80.
Katuli, M. (2018, March 28). Young artists use fashion and data to promote dialog on sexual health. Medium.
Kelley, R. D. (2002). Freedom dreams: The Black radical imagination. Beacon Press.
Kennedy, H. & Hill, R. L. (2018). The Feeling of numbers: Emotions in everyday engagements with data and their
visualisation. Sociology, 52(4), 830-848.
LaMar, T., & Boaler, J. (2021). The Importance and emergence of K-12 DS. Phi Delta Kappan, 103(1), 49-53.
Lee, V. R., Wilkerson, M. H., & Lanouette, K. (2021). A Call for a humanistic stance toward k12 DS education.
Educational Researcher, 50(9), 664-672.
Lupton, D. (2017). Feeling your data: Touch and making sense of personal digital data. New Media & Society, 19(10), 1599-
Lunn, S., Zahedi, L., Ross, M., & Ohland, M. (2021). Exploration of intersectionality and computer science demographics:
Understanding the historical context of shifts in participation. ACM Transactions on Computing Education (TOCE), 21(2), 1-
Malinverni, L., & Pares, N. (2014). Learning of abstract concepts through full-body interaction: A Systematic review.
Educational Technology & Society, 17(4), 100-116.
Martin, D. B. (2003). Hidden assumptions and unaddressed questions in mathematics for all rhetoric. The Mathematics
Educator, 13(2), 721.
Martin, D. B., Price, P. G., & Moore, R. (2019). Refusing systemic violence against Black children: Toward a Black
liberatory mathematics education. In J. Davis & C. C. Jett (Eds.), Critical race theory in mathematics education (pp. 32-55).
McGee, E. O., & White, D. T. (2021). Afrofuturism: Reimaging STEM for Black urban learners. In H. R. Milner & K.
Lomotey (Eds.), Handbook of Urban Education (pp. 384-396). Routledge.
Mirra, N., & Garcia, A. (2020). I hesitate but I do have hope: Youth speculative civic literacies for troubled times. Harvard
Educational Review, 90(2), 295-321.
National Council of Teachers of Mathematics. (2021). What is notice and wonder?
Noble, S. U. (2018). Algorithms of oppression: Data discrimination in the age of Google. NYU Press.
Nussbaum, M. (1992). Human functioning and social justice: In defense of Aristotelian essentialism. Political Theory, 20(2),
Onuoha, M. (2016). The Library of missing datasets [Mixed-media installation].
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway
Pangrazio, L., & Selwyn, N. (2019). Personal data literacies: A Critical literacies approach to enhancing understandings of
personal digital data. New Media & Society, 21(2), 419-437.
Paris, D., & Winn, M. T. (Eds.). (2013). Humanizing research: Decolonizing qualitative inquiry with youth and communities.
Sage Publications.
Pedwell, C. & Whitehead, A. (2012). Affecting feminism: Questions of feeling in feminist theory. Feminist Theory, 13(2),
Philip, T. M., Olivares-Pasillas, M. C., & Rocha, J. (2016). Becoming racially literate about data and data-literate about race:
Data visualizations in the classroom as a site of racial-ideological micro-contestations. Cognition and Instruction, 34(4), 361-
Philip, T. M., & Rubel, L. H. (2019). Classrooms as laboratories of democracy: The Role of new quantitative literacies for
social transformation. In L. Tunstall, G. Karaali, & V. I. Piercey (Eds.), Shifting contexts, stable core: Advancing quantitative
literacy in higher education (pp. 215-223). Mathematical Association of America Press.
Philip, T. M., Schuler-Brown, S., & Way, W. (2013). A Framework for learning about big data with mobile technologies for
democratic participation: Possibilities, limitations, and unanticipated obstacles. Technology, Knowledge and Learning, 18(3),
Rakotondravony, N. (2019, November 1). Power to the powerless: An Interview with Mona Chalabi. Medium.
Rick. (Host) (2021, January 11). What if Natives won? (Episode 62) [Audio podcast episode]. In Decolonized Buffalo.
Podcast Addict.
Roberts, J., & Lyons, L. (2020). Examining spontaneous perspective taking and fluid self-to-data relationships in informal
open-ended data exploration. Journal of the Learning Sciences, 29(1), 32-56.
Rubel, L. [@Laurie_Rubel]. (2020, Oct 18). I suggest that we expand #noticewonder protocol to NOTICE FEEL WONDER
ACT. the feel and the act signal our humanity, and that emotion and agency matter along with reason. could it go in some
other sequence? sure. [Tweet]. Twitter.
Rubel, L. H., Hall-Wieckert, M., & Lim, V. Y. (2016a). Teaching mathematics for spatial justice: Beyond a victory narrative.
Harvard Educational Review, 86(4), 556-579.
Rubel, L. H., Hall-Wieckert, M., & Lim, V. Y. (2017). Making space for place: Mapping tools and practices to teach for
spatial justice. Journal of the Learning Sciences, 26(4), 643-687.
Rubel, L. H., Herbel-Eisenmann, B., Peralta, L. M., Lim, V. Y, Jiang, S., & Kahn, J. B. (2022). Intersectional feminism to
reenvision mathematical literacies & precarity. Research in Mathematics Education, 24(2), 224-248.
Rubel, L. H., Lim, V. Y., Hall-Wieckert, M., & Sullivan, M. (2016b). Teaching mathematics for spatial justice: An
Investigation of the lottery. Cognition and Instruction, 34(1), 1-26.
Rubel, L. H., Nicol, C., & Chronaki, A. (2021a). A Critical mathematics perspective on reading data visualizations:
Reimagining through reformatting, reframing, and renarrating. Educational Studies in Mathematics, 108(1), 249-268.
Rubel, L. H., Peralta, L. M., Herbel-Eisenmann, B., Jiang, S., Kahn, J., & Lim, V. (2021b). Theorizing data science
education: An intersectional feminist perspective on data, power, and playing the game. Proceedings of the 43rd Annual
Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (PME-NA)
(pp. 217-221).
Rumack, A. M., & Huinker, D. (2019). Capturing mathematical curiosity with notice and wonder. Mathematics Teaching in
the Middle School, 24(7), 394-399.
Swalwell, K. M. (2013). Educating activist allies: Social justice pedagogy with the suburban and urban elite. Routledge.
Stornaiuolo, A. (2020). Authoring data stories in a media makerspace: Adolescents developing critical data literacies. Journal
of the Learning Sciences, 29(1), 81-103.
Tygel, A. F., & Kirsch, R. (2016). Contributions of Paulo Freire for a critical data literacy: A Popular education approach.
The Journal of Community Informatics, 12(3).
Van Wijnendaele, B. (2011). Social justice and the politics of emotions. Human Geography, 4(2), 76-90.
Warren, C. A. (2021). About centering possibility in Black education. Teachers College Press.
Wilkerson, M. H., & Polman, J. L. (2020). Situating DS: Exploring how relationships to data shape learning. Journal of the
Learning Sciences, 29(1), 1-10.
Woodson, A. N. (2021). Afropessimism for us in education: In fugitivity, through fuckery and with funk. In C. A. Grant, A.
N. Woodson, & M. J. Dumas (Eds.), The future is Black: Afropessimism, fugitivity, and radical hope in education (pp. 16-
21). Routledge.
... Similarly, in Kahn et al. (2022) high school students worked with local data from their community as they investigated the impact of the lottery in their communities. The work demonstrates the power of educational designs that scaffold learners to take a multimodal approach using mathematical, quantitative, and qualitative data reasoning. ...
... Another approach to fostering data literacy justice is in elevating emotion and embodiment through creative practices, which leverage multiple ways of knowing to communicate more effectively and equitably with data (D'Ignazio & Klein, 2020). In Kahn's et al. (2022) framework, Notice, Wonder, Feel, Act, and Reimagine, the authors highlight a procedure for integrating affect into one's approach to data. Three of the contributions in particular demonstrate how creating non-canonical representations engaged learners in new ways of sensemaking and communicating with data. ...
... In Kahn et al. (2022) high school students, having calculated the minuscule chances of winning the lottery, used parody, analogies, and satire to communicate their findings, redesigning an existing lottery advertisement titled "Hey, You Never Know," to "Hey, Now You Know." The authors highlight how these rhetorical strategies were critical for learners to reimagine new futures for their community. ...
Full-text available
With growing awareness of, and attention to, the potential of data to inform decisions across contexts, has come an increasing recognition and need to develop data literacy strategies that support people to learn to be critical of data, given this consequential nature of data use (and abuse). To achieve a just society, inequities in both capacity for data literacy, and the applications of data in society, must be addressed. A key aim is to create learning experiences that engage learners with issues of power and inequity, including those typically marginalized by data literacy education. In this way, data literacy and social justice learning goals are intertwined, and mutually supportive, in developing data literacy in learning about, through, and for social justice. This special issue assembles five empirical studies on learning at the intersection of data literacy and social justice, and that illustrate various approaches to intertwining data science and social justice learning goals. They moreover highlight the importance of the learning sciences as a perspective for understanding how people learn in specific contexts of data justice. This essay reflects on themes raised by these contributions, and offers a framework for conceptualizing the intersections between the learning of data literacy and justice. In particular, we draw on existing distinctions between "reading" and "writing the world," and propose a mapping of data literacy justice activities from data comprehension to participation, and from thin to thick justice.
Full-text available
Data visualizations have proliferated throughout the COVID-19 pandemic to communicate information about the crisis and influence policy development and individual decision-making. In invoking exponential growth, mathematical modelling, statistical analysis, and the like, these data visualizations invite opportunities for mathematics teaching and learning. Yet data visualizations are social texts, authored from specific points of view, that narrate particular, and often consequential, stories. Their fundamental reliance on quantification and mathematics cements their social positioning as supposedly objective, reliable, and neutral. The reading of any data visualization demands unpacking the role of mathematics, including how data and variables have been formatted and how relationships are framed to narrate stories from particular points of view. We present an approach to a critical reading of data visualizations for the context of mathematics education that draws on three interrelated concepts: mathematical formatting (what gets quantified, measured, and how), framing (how variables are related and through what kind of data visualization), and narrating (which stories the data visualization tells, its potential impacts and limits). This approach to reading data visualisations includes a process of reimagining through reformatting, reframing and renarrating. We illustrate this approach and these three concepts using data visualizations published in the New York Times in 2020 about COVID-19. We offer a set of possible questions to guide a critical reading of data visualizations, beyond this set of examples.
Full-text available
This paper reports on progress in the development of a teaching module on machine learning with decision trees for secondary-school students, in which students use survey data about media use to predict who plays online games frequently. This context is familiar to students and provides a link between school and everyday experience. In this module, they use CODAP's “Arbor” plug-in to manually build decision trees and understand how to systematically build trees based on data. Further on, the students use a menu-based environment in a Jupyter Notebook to apply an algorithm that automatically generates decision trees and to evaluate and optimize the performance of these. Students acquire technical and conceptual skills but also reflect on personal and social aspects of the uses of algorithms from machine learning.
There is growing interest in how to better prepare K–12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are shaped by a combination of personal experiences, cultural tools and practices, and political concerns. We demonstrate through two examples how our proposed humanistic stance highlights ways that efforts to make data personally relevant for youth also necessarily implicate cultural and sociopolitical dimensions that affect the design and learning opportunities in data-rich learning environments. We offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.
The COVID-19 global pandemic has required everyone to make sense of data about community spread, levels of risk, and vaccine efficacy. Yet research shows that students are underprepared in data literacy. Tanya LaMar and Jo Boaler argue that data science education provides an opportunity to address this problem while providing much needed updates to the current mathematics curriculum. The integration of data science can provide a more equitable mathematics pipeline than the calculus-focused pathway that has excluded most students from a future in mathematics. Through data science, students can learn to answer questions that are relevant to their lives and communities, to be critical consumers of the data that surround them every day, and to wield the power of data analysis.
Although computing occupations have some of the greatest projected growth rates, there remains a deficit of graduates in these fields. The struggle to engage enough students to meet demands is particularly pronounced for groups already underrepresented in computing, specifically, individuals that self-identify as a woman, or as Black, Hispanic/Latinx, or Native American. Prior studies have begun to examine issues surrounding engagement and retention, but more understanding is needed to close the gap, and to broaden participation. In this research, we provide quantitative evidence from the Multiple-Institution Database for Investigating Engineering Longitudinal Development—a longitudinal, multi-institutional database to describe participation trends of marginalized groups in computer science. Using descriptive statistics, we present the enrollment and graduation rates for those situated at the intersection of race/ethnicity and gender between 1987 and 2018. In this work, we observed periods of significant flux for Black men and women, and White women in particular, and consistently low participation of Hispanic/Latinx and Native American men and women, and Asian women. To provide framing for the evident peaks and valleys in participation, we applied historical context analysis to describe the political, economic, and social factors and events that may have impacted each group. These results put a spotlight on populations largely overlooked in statistical work and have the potential to inform educators, administrators, and researchers about how enrollments and graduation rates have changed over time in computing fields. In addition, they offer insight into potential causes for the vicissitudes, to encourage more equal access for all students going forward.
Paulo Freire is the patron of education in Brazil. His main work - the Popular Education pedagogy - influences many educators all over the world who believe in education as a way of liberating poor oppressed people. One of the outcomes of Freire's work is a literacy method, developed in the 1960's. In this paper, we propose the adoption of elements of Freire's Literacy Method for use in a pedagogical pathway towards data literacy. After tracing some parallels between literacy education and data literacy, we suggest some data literacy strategies inspired on Freire's method. We also derive from it a definition for critical data literacy.
Open large-scale datasets (LSDS) and data visualization technologies are new cultural tools that have potential to inform public dialogue and learning about important socioeconomic and scientific matters, particularly if the data is used to consider both personal and shared experiences. This paper reports on a design study in which diverse middle and high school youth in a free summer workshop at an urban public library were asked to model their family geobiographies, or their personal family migration stories, with socioeconomic LSDS. Youth represented family decision-making and social conditions that might have motivated family movements with online, dynamic data modeling and mapping tools (; The qualitative video analysis examined participants’ experiences and learning in storytelling and modeling the family geobiography, focusing on multimodal talk-in-interaction to understand how the study design engaged learners’ capacities, histories, and imagined futures in relation to the LSDS and supported learning about oneself and society. Grounded examples from two family cases are used to illustrate how participants placed their families and selves into data ontologies and the role of family members in composing storylines.
Race and racism are defining characteristics of life in American society. Technology and other knowledge production tools are created within this racist context and generate information that, when (mis)used, worsen inequities for already marginalized people. Race after Technology takes readers on a provocative journey into technology’s role in perpetuating racism. In this book, Benjamin explores the myriad of ways in which technology reinforces systemic oppression in America, creating a digital dragnet which codes people by stigmatizing them for where they live, work, and play. Technology codifies discriminatory practices in a way that results in racist responses to social problems. It succeeds by excluding some groups from taking part in its design, intent, implementation, and results. While it strives to present with an “allure of objectivity,” the content of technology reflects the biases of its creators and the motivations of its sponsors. It twists society like a fun-house mirror where the principles of Jim Crow and the “illusion of [racial] progress” persist.
Data visualization technologies are powerful tools for telling evidence-based narratives about oneself and the world. This paper contributes to the literature on data science education by examining the sociotechnical practices of data wrangling—strategies for selecting and managing large, aggregated datasets to produce a model and story. We examined the learning opportunities related to data wrangling practices by investigating youth’s talk-in-interaction while assembling models and stories about family migration using interactive data visualization tools and large socioeconomic datasets. We first identified ten sociotechnical practices that characterize youth’s interaction with tools and collaboration in data wrangling. We then suggest four categories of activities to describe patterns of learning related to the practices, including addressing missing data, understanding data aggregation, exploring social or historical events that constitute the formation of data patterns, and varying data visual encoding for storytelling. These practices and activities are important to understand for supporting future data science education opportunities that facilitate learning and discussion about scientific and socioeconomic issues. This study also sheds light on how the family migration modeling context positions the youth as having agency and authority over the data and contributes to the design of CSCL environments that tackle the challenges of data wrangling.