City Digits: Local Lotto: Developing Youth Data Literacy by Investigating the Lottery

Abstract

Data literacy—the ability to work with, analyze, and make arguments with data—is essential in a data-driven society and can be instrumental toward engaging powerfully with civic issues.1 This paper illustrates how City Digits: Local Lotto, a high school curriculum and supporting web application, supported students in building data literacy. Local Lotto included opportunities for qualitative data collection and quantitative analysis, enabling youth to develop data-informed arguments about the lottery’s impact on local communities.

Introduction

In the last decade, the use of data for civic decision making has increased exponentially.2 From transportation planning to allocation of health care funding, the ability to work with data has become essential to engage in civic policy making.3 The public needs data literacy to understand civic issues and to be able to advocate for civic change.4 Data literacy is essential for all individuals. Therefore, integration of data literacy into K–12 education is important for a generation that increasingly depends on data analytics.

Data literacy encompasses the skills and knowledge necessary to ask data-centric questions, analyze data to answer questions, create, and interpret representations of data to build arguments, develop and test inferences, and use data to solve problems or communicate solutions.5 Data literacy is similar to quantitative literacy in that it implies the ability to reason critically, statistically, and mathematically6 but extends beyond quantitative literacy by emphasizing fluency with both quantitative and qualitative information. Data literacy relies on an exploratory inquiry process of data collection and analysis7 and implies an understanding of the logic and ethics involved in data-driven decision-making.

Teaching data literacy skills to young people is challenging because data can be hard to contextualize.8 Youth-based participatory sensing and mapping projects that focus on collecting or working with data about local-based social issues increase data literacy skills. These projects show that students better understand data when they are active participants in its collection.9 Given the growing importance of giving youth opportunities to develop data literacy and new possibilities for using local data to respond to civic issues, the City Digits research team developed a web-based data exploration tool and curriculum to teach data literacy skills to youth, with special interest in underserved, urban neighborhoods. City Digits: Local Lotto gave students the opportunity to investigate quantitative and qualitative data about the state lottery with interactive maps and participatory data collection. This paper demonstrates how Local Lotto supported the development of data literacy in youth, in terms of their generating opinions about the lottery and justifying those opinions with data. Beyond supporting youth to form opinions about a local and socially relevant topic, Local Lotto’s interactive format led to new forms of equitable classroom participation and learning.

The Open Data Movement Needs Data Literacy

There is a current movement to share data once tightly controlled by government agencies. Several cities in the United States, including New York City, San Francisco, and Chicago, opened offices specifically to respond to the public’s demand for accessible city data.10 Open access to information, as well as the emergence of low-cost or free analysis web tools, allows citizens to look for patterns in government activity or to use data analysis to advocate for change.11 Beyond individual citizens or journalists, the open data movement enables organizations to build services that draw on government data, thereby creating new uses for the information.12

Governments share data with the intention of transparency,13 but having access to data is not the equivalent of being data literate. The ability to work with data requires access to education, infrastructure, and technology and is therefore more accessible to individuals who have greater access to resources.14 The open data movement could further marginalize communities. This is because communities without high data literacy skills would not be able use data for action in the same way other comminutes might.15 For this reason, data literacy education is particularly important in under-served neighborhoods.

Schools can play an essential role in bridging the capacity divide and equipping youth with the skills necessary to understand and work with data, especially in under-served neighborhoods where youth lack access to other educational resources.16 In a society heavily impacted by data, educational interventions have the potential to make significant positive impacts on the long-term prospects of young people. Teaching youth to work with data is particularly important in underserved communities, where data literacy can provide young leaders with the skills necessary to tackle social issues affecting their neighborhoods.

Teaching Data Literacy with Participatory Media

The development of participatory media tools for youth based data literacy projects has the potential to help students make better connections to the data they are analyzing, thereby increasing data literacy. This is because participatory media often allow youth to collect and interact with their own data, thereby providing a way to contextualize the information. The use of community-based data in participatory media projects has also shown an increased awareness of civic issues. For the purposes of this study, we are defining participatory media as participatory sensing, participatory mapping, and participatory action research.

Participatory Sensing

In participatory sensing projects, youth use mobile technologies to collect quantitative data and share their results with the public, increasing local knowledge on topics as wide ranging as air quality to bird counts.17 This strategy for data collection, which has its roots in Public Participation Scientific Research (PSSR), encourages open scientific inquiry in order to increase youth engagement in science learning and strengthen mathematical reasoning.18 Participatory sensing projects are powerful for teaching data literacy because when students design data collection processes they acquire a greater understanding of what data means and how to use it for analysis.19 Mobilize, a participatory sensing project developed by the University of California, Los Angeles’s Center for Embedded Network Sensing (CENS) demonstrated that collecting real-world data gave students a greater ability to address data errors, unit issues, and visualization strategies.20 Mobile Application Development for Science (MAD Science), a participatory sensing project developed by a research team at the University of North Carolina at Charlotte, showed that students increased their knowledge of water quality measurements units through their testing of water in a local stream. Participatory sensing projects have also shown that they help support student participation and performance in related course material.21 Participatory sensing projects encourage young people to learn about their environment, share their knowledge with their community, and develop data literacy skills through first-hand collection, interpretation, and presentation of data.

Participatory Mapping

Participatory mapping, in which the public is engaged in contributing map data, can illuminate complex social justice issues.22 Participatory mapping projects that combine both quantitative and qualitative data are especially effective for teaching data literacy because they engage students who have different strengths and allow students to contextualize their interpretations of quantitative data with human-centered narratives.23 A participatory mapping curriculum developed by Sarah Elwood and Katharyne Mitchell at the University of Washington enabled students to revisit archived data, generate opinions, and start conversations about their data. Students showed increased engagement in classroom work as well as a heightened interest in civics.24 Participatory mapping programs help students to justify opinions with evidence, an important component of data literacy.

Participatory Action Research

A third category of participatory media is Youth Participatory Action Research (YPAR) projects, in which students collect and analyze both quantitative and qualitative data. YPAR offers participants opportunities to engage in civic issues affecting their own lives by studying problems in their community and developing actions to address those problems.25 For instance, in the “Echoes of Brown” project, organized by the Public Science Project, youth from racially integrated urban and suburban high schools learned how to conduct interviews, design surveys, and organize the data they collected in order to analyze intergroup relations and the opportunity gap.26 YPAR’s hands-on approach to investigating civic issues increases students’ motivation to engage with data and develop data literacy skills.

City Digits: Local Lotto

The City Digits research team designed Local Lotto, a mathematics curriculum that incorporates data collection and analysis methods informed by participatory media projects for use in New York City high schools, especially in schools where mathematics scores are persistently low. The curriculum focuses on the lottery because it is a locally relevant topic with social justice ramifications. The curriculum is comprised of four sections that use an interactive web tool. First, students learn how to calculate the probability of winning a jackpot lottery game. Next, they conduct and collect interviews of lottery players and retailers in their neighborhood using the Local Lotto tool on mobile tablets. Third, students analyze citywide and local level lottery data obtained from the New York State Lottery Commission and public data from the 2010 Census, using an interactive map. Finally, students synthesize qualitative interview data with quantitative map data to formulate their own opinions about the lottery’s social impact. Using the web-tool, students create multimedia narratives called “tours” to teach others about what they learned. Developing the tours allows students to synthesize their data explorations and form opinions, which is an important component of data literacy.

Local Lotto focuses on teaching data literacy by combining quantitative data, in the form of thematic maps, together with qualitative data, in the form of student collected interviews and photographs. Blending quantitative and qualitative investigations enables a more nuanced analysis of the lottery,27 and the use of maps helps reveal relationships between geography and social issues.28 In Local Lotto, thematic maps highlight the social dynamics of the lottery by visually communicating quantitative data overlaid on a map of the city by neighborhood. Ultimately, the maps show a pattern of more lottery spending relative to income, in low-income neighborhoods. Photographs of the local streetscapes and interviews with pedestrians allow for an interpretation of this pattern from first-person perspectives.29 Combining geographic analysis with images and interviews contextualizes data and allows for the investigation of a civic issue from multiple vantage points.

Students Collect Narratives from the Community

Local Lotto replicates aspects of participatory sensing, mapping, and action research projects by including students in the process of data collection. Using the Local Lotto web-tool, students collect geo-registered narratives from lottery players and retailers in order to learn how the lottery affects people every day. Small teams of students canvass the school’s neighborhood to interview pedestrians and local storekeepers (fig. 1). Students collect data about lottery purchase habits, the volume of sales at neighborhood stores, and their neighbors’ experiences with and opinions about the lottery. Mobile tablets, as figures 2 and 3 show, allow students to navigate, collect data in the field, and instantly publish the geo-located interviews. The local investigations support students in acquiring data collection and interviewing skills and make the issues surrounding the lottery come to life.

Figure 1. Students conduct interviews in their neighborhood.

Figure 1. Students conduct interviews in their neighborhood.

Figure 2. A player interview includes geographic location, photo, and audio and text responses.

Figure 2. A player interview includes geographic location, photo, and audio and text responses.

 Figure 3. Interview points appear on a map that also reveals lottery data about the neighborhood.

Figure 3. Interview points appear on a map that also reveals lottery data about the neighborhood.

Lottery Data Contextualized by Geography

In the second phase of the curriculum, students analyze an interactive map of the city to address questions about the lottery’s local and citywide impact. The Local Lotto maps show 2010 New York State lottery spending and winning data at a city level, neighborhoods (fig. 4), and street level (fig. 5). Students use the tool to compare lottery sales data from individual stores, as well as differences in overall neighborhood lottery spending patterns. The interactive maps help communicate the data by allowing students to visualize geographic patterns. The maps show that low-income neighborhoods spend a higher proportion of their income on lottery tickets than other neighborhoods. Clicking on the various neighborhoods triggers information windows that explain relationships between income, lottery sales, and lottery winnings. Zooming into the street level of the map reveals lottery sales and prize amounts for every store in the city. The data shows that almost all the stores sell more lottery tickets than they pay out in prize money.

Figure 4. Map of percent income spent on lottery. The darker blue represents areas where a greater percentage of median household income is spent on lottery tickets.

Figure 4. Map of percent income spent on lottery. The darker blue represents areas where a greater percentage of median household income is spent on lottery tickets.

Figure 5. Map of net gain or loss, zoomed into the street level. The green circles represent the amount of money spent on lottery tickets at individual retail stores. The purple circles represent the amount of money won from lottery tickets at the same stores.

Figure 5. Map of net gain or loss, zoomed into the street level. The green circles represent the amount of money spent on lottery tickets at individual retail stores. The purple circles represent the amount of money won from lottery tickets at the same stores.

Constructing Arguments with Data

In the final phase of the curriculum, students synthesize knowledge drawn from interviews, map data, lessons on probability, and the use of lottery profits to create opinions about the lottery, called “tours.” In the process, students share, debate, and create multimedia narratives to illustrate their reflections on the lottery, which they publish to the website. Creating tours supports the development of data literacy, because youth must use data to form and justify opinions. By the time students create tours, they are “experts” on the topic and can confidently share their opinions and knowledge with others. Since the tool is online and public, students can share their work with their classmates, families, neighbors, and students at other schools (fig. 6). Sharing their reflections enables students to teach others and allows for the creation of a diverse learning community.

Figure 6. Page displaying students' arguments.

Figure 6: Page displaying students’ arguments.

Analyzing Local Lotto

Local Lotto was piloted at a Brooklyn high school with a curricular focus on social justice. This school is in one of the city’s lowest income neighborhoods; all students at the school qualify for free or reduced lunch. Nearly half of the students are immigrants and learners of English, and nearly half have special education status or were previously held back in school. School students are too young to buy lottery tickets, but lottery advertisements and vending machines dominate the urban landscape around the school.

The initial pilot was with one teacher and a single advisory class in April 2013. The curriculum and tools were revised, and a larger pilot was conducted in November 2013 with another teacher and his four twelfth grade mathematics classes of ninety-five students. This article focuses on findings from the second, larger pilot. School attendance in general is a serious challenge, with about half of the participating students missing fifteen or more days of school in the previous year. Although these students were high school seniors, most of them (79 percent) had not attained a college-ready score on the state’s entry-level algebra exam. The school was experimenting with mandatory mathematics class for seniors with a goal of integrating social justice theme into mathematics teaching and learning at the school.

We wrote detailed field notes taken in class observations and produced analytic memos about each class session. We gathered students’ written products from all of the class sessions, the data they gathered in their neighborhood using the web tool, and the tours they posted. We also conducted pre- and post-interviews with a student focus group of four students and surveyed all students about their experiences in the sessions.

In the sections that follow, we discuss three findings that emerged from this analysis:

  1. Youth successfully negotiated across categories of information to form opinions about a civic issue, a key element towards achieving data literacy.
  2. Students’ ability to interpret the various forms of data representation affected their ability to use that data to support their opinions.
  3. By providing multiple pathways to engage with the curriculum and associated digital tools, Local Lottocreated new equitable classroom participation opportunities.

Student Produced Arguments Show Data Literacy

We largely draw our findings about students’ use of data through an analysis of the student developed tours from the second iteration of piloting. (See Appendix A.) There were a total of sixteen tours produced in small groups across the four classes. The diversity of student-produced positions on the lottery demonstrate that students were able to formulate and express their own opinions supported by quantitative and qualitative data, an important skill for deepening data literacy.

The positions represented in the tours ranged from supporting (five out of sixteen) to critiquing (eleven out of sixteen) the lottery, with numerous different reasons cited for these stances. For example, one group (figs. 7–8) chose to use what they learned to challenge the statement that the lottery is a “tax on the mathematically illiterate.” These students argued that it is actually “omission of the truth” that encourages lottery spending rather than a lack of mathematical skills. They pointed to the state lottery website to show how the presentation of data on the web site is manipulated to highlight the odds of winning. They presented data that showed their neighborhood’s lottery spending against other neighborhoods. They concluded that all communities lost money to the lottery and that since this information is not advertised to the public, the lottery is an unfair system.

Figure 7. A group of students works together on constructing their arguments.

Figure 7. A group of students works together on constructing their arguments.

Figure 8. This student tour explains that lottery marketing represents an "omission of the truth."

Figure 8. This student tour explains that lottery marketing represents an “omission of the truth.”

Another group critiqued the lottery system using lottery spending maps to argue that the lottery is a “scam because it targets low income areas.” They used their interpretations of qualitative findings to explain, “[P]eople behind it know that the poorer the people, the more likely they are to spend money in hopes of making more because of the lifestyle that they live.”

Supporters of the lottery argued that it is a fair and effective method of fundraising. They came to this opinion because the proceeds benefit the educational system and people are free to choose whether to contribute by buying lottery tickets. To build their argument, this group integrated what they had learned about the distribution of lottery proceeds (38 percent of lottery ticket proceeds go toward education in New York State) and used data and maps to demonstrate the magnitude of this amount. After reviewing different quantitative variables on the map, they eventually shaped the argument that the lottery is a significant resource for schools. Furthermore, the group pointed to several interviews conducted with neighbors who chose not to spend their money on the lottery because they knew they were unlikely to win. The students argued that playing the lottery is a personal choice and should not be considered a tax.

Students’ Use of Data is Dependent on Type of Data Representation

Although students were able to use quantitative and qualitative data to form various opinions about the lottery, they demonstrated greater proficiency in working with certain types of data representations over others. Students worked with local level data more often in their arguments, as it was easier for them to connect with. Most of the tours (twelve out of sixteen) incorporated local-level maps to present their arguments. Students were also able to use multiple approaches in working with these maps. Of the twelve tours that incorporated local-level maps, eight presented the gain and loss data from a singular neighborhood to show that lottery spending is greater than winning. Another tour focused on the data of a particular store with greater gain than loss as an example of an outlier. While most groups argued that a greater loss than gain indicated that the lottery was a “scam” or “rigged,” one group took the perspective that the lottery is an effective way to raise money for education as the maps showed high lottery revenues. The group showcasing the example of an outlier made a connection to qualitative information by arguing that stores with large winnings generate a sense of hope, which keeps people playing.

On the other hand, students struggled to make successful arguments with the data using citywide maps. One of the groups displayed a screenshot of part of New York City to claim that people in Manhattan and Queens spend less on the lottery than people in Brooklyn and the Bronx. Yet the map did not show all four of these boroughs and there was no explanation for how the maps illustrate this idea. Another group chose to show the data of two different neighborhoods, one of higher and one of lower income, to explain that people of lower income spend more money on the lottery. However, this group did not explain or interpret the data they were showing. They also used the wrong type of thematic map to illustrate their idea. It is likely that students’ difficulty working with maps was a consequence of their lack of both experience with the use of thematic maps and an understanding of New York City’s geography.

Students incorporated interview data in a variety of ways to support and enrich their opinions about the lottery. Of sixteen group tours that students created across four class sections, eleven referenced interviews in their tours. Five of these groups used interviews to showcase individuals in the community who echoed the students’ own opinions that the lottery is not worth playing because the chances of winning are so unlikely. Students also noted this opinion as their greatest learning experience (seven out of forty-seven) in post-curriculum surveys as well. Another four groups used interview data as evidence to support both quantitative and qualitative statements about the lottery. For example, two groups used data they collected from interviews to show that individuals are losing more money than winning. Another group illustrated the benefits of the lottery by citing an interview with a lottery player who said he would use the winnings to help others. Students used interviews to add personal narratives to their analysis of the social impact of the lottery.

Multiple Pathways for Participation

The focus group data provided additional insights into how students interacted with the curriculum. Our main finding was that the curriculum opened up multiple pathways for participation by engaging students in novel ways. The curriculum’s focus on a local context drew students’ interest and created new types of opportunities for students to contribute and learn. This finding is similar to other participatory media projects, which have shown that the use of local data increased an interest in learning course materials overall.

Students found the curriculum provided real world data and exercises that challenged them and made the curriculum more exciting. Students expressed that they were interested in working on an issue that was “real”30 and liked using technologies to explore datasets and maps that focused on their local geographies. Another student described how students in the class “didn’t play the fool” and that he had “never seen a class even work like this even work like this, ever, ever before.… Everyone was so interested. I didn’t think that was possible.” Students also shared that they were engaged in the Local Lotto sessions because the issues were so complex and demanded higher levels of reasoning than their typical classes. One student told us, “[In regular math classes] the teacher gives you a paper and explains it, then you probably finish the paper before she’s done talking, and you sit and talk for the rest of class. It’s boring. It be boring to me [sic].” Local Lotto, though, was different.

Local Lotto generated unexpected participation opportunities for students during their field work in the community. Interview data collection brought the students into contact with many Spanish speakers, a contrast to the dominant language of English in the school and the classroom. Because understanding Spanish was quickly crucial in the field research, this positioned any Spanish-speaking students as group leaders. Not only could they understand the information that was shared with them, but also they could translate it into English to share with the class. For example, a student who is a new immigrant and was quiet during the initial class sessions became outgoing and animated during the qualitative data collection. She conducted the majority of the interviews for her group and was excited to contribute during the classroom interview debrief. She emerged as a vocal participant in subsequent class sessions.

The digital tools invited students to interpret the data visualizations, compare data numerically, synthesize different types of data, and produce insightful arguments supported by data. These arguments extend beyond the walls of the classroom. For example, students reported that they brought their findings home to their families in an attempt to teach their parents something they had learned in school. In reflecting about his experiences related to this project, one student explained, “It was something that I never experienced before—it’s just something new—that can help me in my local environment, in my house actually. I’m really trying to get my mother to stop playing.” This example demonstrates that students are learners but quickly take on roles of expert, advocate, or educator in their own homes and communities. The lottery had personal meaning to the students and their families, and the data that they analyzed was about their own communities in context of their own city.

Conclusions

Because cities are collecting and opening up data to the public at unprecedented rates, there is an increasing need for individuals to become data literate so that they can participate in and critique civic decision-making. Local Lotto explored methods of developing data literacy among youth in underserved neighborhoods by focusing on the need to bring together qualitative and quantitative data using methods from participatory sensing, mapping, and action research. Overall, participating students were successful in using qualitative and quantitative data and representations as evidence for their own opinions about the social effects of the lottery system, demonstrating increased data literacy, and understanding of a complex civic topic. Using a socially relevant topic to teach data literacy generated a nuanced classroom debate. Each group of students formed a unique perspective on the lottery, which helped the class understand that data can have multiple interpretations. City Digits was not successful in all of its goals: students who were unfamiliar with city-scale thematic maps struggled to use map representations of data as evidence for their arguments. Future curriculum will address these issues by spending more time orienting students to map data.

The interactive and participatory nature of the curriculum and tool allowed youth to deeply explore data they could connect with. Students indicated that they worked harder in the class than they do in regular mathematics classes because the content was challenging and relevant to their own lives. English language learner students, for whom participation is typically a challenge, were able to take leadership roles in the data collection. Some students reflected on the personal significance of their learning during Local Lotto and indicated that they shared their findings at home with family members who regularly buy lottery tickets.

City Digits’ goal was to support students in developing data literacy skills to be informed citizens and gain experience in building data-backed arguments about a real world issue. Data literacy skills empower youth with the ability to voice their opinions supported by data and open up the potential of participating in civic decision-making.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. DRL-1222430. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. DRL-1222430. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This project could not have been conducted without the work and creativity of researchers at the Civic Data Design Lab at MIT, including Liqun Chen, Benjamin Golder, Karuna Mehta, Chris Rhie, and Alicia Rouault. In addition, this project was conducted in collaboration with The Center for Urban Pedagogy (Valeria Mogilevich, Jose Ojeida, and Pema Domingo-Barker); high school teachers, Lauren Shookhoff and Mathew Sullivan; and Kellyn Morris, doctoral student from the University of Maryland.

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Notes    (↵ returns to text)

  1. Thomas M. Philip, Sarah Schuler-Brown, and Winmar Way. “A Framework for Learning About Big Data with Mobile Technologies for Democratic Participation: Possibilities, Limitations, and Unanticipated Obstacles.” Technology, Knowledge and Learning 18, no. 3 (2013): 103-120.
  2. Philip, Schuler-Brown, and Way.
  3. Bernie Trilling and Charles Fadel. 21st century Skills: Learning forLlife in Our Times (New York: John Wiley & Sons, 2009).
  4. Janna Quitney Anderson, “Big Data: Experts Say New Forms of Information Analysis Will Help People Be More Nimble and Adaptive, but Worry over Humans’ Capacity to Understand and Use These New Tools Well,” Pew Research Internet Project: Future of the Internet (July 20, 2012), http://www.pewinternet.org/files/old-media/Files/Reports/2012/PIP_Future_of_Internet_2012_Big_Data.pdf.
  5. “21st Century Skills Map,” Partnership for 21st Century Skills, last modified May 2009, accessed October 31, 2014, http://www.p21.org/storage/documents/21stcskillsmap_geog.pdf.
  6. Lynn Arthur Steen, Mathematics and Democracy: The Case for Quantitative Literacy (Princeton, NJ: Woodrow Wilson National Fellowship Foundation, 2001).
  7. Schutt, Rachel, “Taking a Chance in the Classroom: Embracing the Ambiguity and Potential of Data Science,” CHANCE 26, no. 4 (2013): 46-51.
  8. National Research Council, The Mathematical Sciences in 2025 (Washington, DC: The National Academies Press, 2013).
  9. Scott Heggen, Osarieme Omokaro, and Jamie Payton, “Mad Science: Increasing Engagement in STEM Education through Participatory Sensing,” paper presented at UBICOMM 2012, The Sixth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, Barcelona, Spain, September 23-28, 2012.
  10. Brett Goldstein, Beyond Transparency: Open Data and the Future of Civic Innovation, with Lauren Dyson(San Francisco: Code for America Press, 2013), http://beyondtransparency.org/pdf/BeyondTransparency.pdf.
  11. Ian Kalin, “Open Data Policy Improves Democracy,” SAIS Review of International Affairs 34, no. 1 (2014): 59-70.
  12. Francisca M. Rojas, Transit Transparency: Effective Disclosure through Open Data (Cambridge, MA: Transparency Policy Project, 2012), http://reconnectingamerica.org/assets/Uploads/20120828FINALUTCTransitTransparency8-28-2012.pdf.
  13. John Carlo Bertot, Paul T. Jaeger, and Justin M. Grimes, “Promoting Transparency and Accountability through ICTs, Social Media, and Collaborative e-Government,” Transforming Government: People, Process and Policy 6, no. 1 (2012): 78-91.
  14. Anthony M. Townsend, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia (New York: W.W. Norton & Company, 2013).
  15. Michael B. Gurstein, “Open Data: Empowering the Empowered or Effective Data Use for Everyone?” First Monday 16, no. 2 (2011).
  16. Antero Garcia and Ernest Morrell, “City Youth and the Pedagogy of Participatory Media,” Learning, Media and Technology 38, no. 2 (2013): 123-127.
  17. Jeffrey A. Burke et al., “Participatory Sensing” (paper presented at World Sensor Web Workshop, ACM Sensys 2006, Boulder, CO, October 31, 2006), http://remap.ucla.edu/jburke/publications/Burke-et-al-2006_Participatory-sensing.pdf.
  18. Tytler, Russell, et al., Opening up Pathways: Engagement in STEM across the Primary-Secondary School Transition (Canberra, Australia: Australian Department of Education, Employment and Workplace Relations, 2008).
  19. Heggen, Omokaro, and Payton.
  20. Jane Margolis, Joanna Goode, and David Bernier, “The Need for Computer Science,” Educational Leadership 68, no. 5 (2011): 68-72.
  21. Heggen, Omokaro, and Payton.
  22. Ioana Literat, “Participatory Mapping with Urban Youth: The Visual Elicitation of Socio-Spatial Research Data,” Learning, Media and Technology 38, no. 2 (2013): 198-216.
  23. Erica Sachiyo Deahl, “Better the Data You Know: Developing Youth Data Literacy in Schools and Informal Learning Environments” (master’s thesis, MIT, 2014), http://dspace.mit.edu/handle/1721.1/89958#files-area.
  24. Katharyne Mitchell and Sarah Elwood, “Engaging Students through Mapping Local History,” Journal of Geography 111, no. 4 (2012): 148-157.
  25. Julio Cammarota and Michelle Fine, eds., Revolutionizing Education: Youth Participatory Action Research in Motion (New York: Routledge, 2010).
  26. María Elena Torre and Michelle Fine. “A Wrinkle in Time: Tracing a Legacy of Public Science through Community Self‐Surveys and Participatory Action Research,” Journal of Social Issues 67, no. 1 (2011): 106-121.
  27. Jick, Todd D. “Mixing Qualitative and Quantitative Methods: Triangulation in Action.” Administrative Science Quarterly 24, no. 4 (1979): 602-611.
  28. Reginald G. Golledge, “The nature of geographic knowledge,” Annals of the Association of American Geographers 92, no. 1 (2002): 1-14.
  29. Seidman, Irving, Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences, 4th ed. (New York: Teachers College Press, 2012).
  30. All quotes in this section taken from interviews conducted with / written feedback from class participants, as well as phrasing taken directly from the students opinions generated as “Tours” on the Local Lotto online tool.
Sarah Williams, Erica Deahl, Laurie Rubel & Vivian Lim

About Sarah Williams, Erica Deahl, Laurie Rubel & Vivian Lim

Sarah Williams is an Assistant Professor of Urban Planning and the Director of the Civic Data Design Lab at MIT. The Civic Data Design Lab develops innovative techniques to collect, distribute, and visualize information to communicate and expose urban policy to broad audiences. Before coming to MIT, Williams was Co-Director of the Spatial Information Design Lab at Columbia University. Williams has won numerous technology and planning awards and her design work has been widely exhibited including work in the Guggenheim and a project that is currently on view in the Museum of Modern Art (MoMA) in New York City.

Erica Deahl is a visual and UX design specialist at 18F, an organization in the General Services Administration that uses agile development and user-centered design to build better digital services for federal agencies. She recently finished an M.S. at MIT, where she researched the use of digital media in K-12 public education to support technological literacies and promote civic engagement. Previously she was a senior designer at 2x4 in New York City, where she designed and managed interactive projects for cultural sector clients.

Laurie Rubel is an Associate Professor of Secondary Education at Brooklyn College of the City University of New York. Formerly a high school mathematics teacher, she has worked with mathematics teachers in New York City since 2003. Her research interests include probabilistic thinking, teaching mathematics for spatial justice, and mathematics teacher education.

Vivian Lim is a research assistant at CUNY. She is a doctoral candidate at the University of Pennsylvania Graduate School of Education with an interest in the role of mathematics curriculum in the civic engagement and development of youth. Vivian formerly worked as a high school mathematics teacher in Brooklyn, NY.

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