Current efforts to build data literacy focus on technology-centered approaches, overlooking creative non-digital opportunities. This case study is an example of how to implement a Popular Education-inspired approach to building participatory and impactful data literacy using a set of visual arts activities with students at an alternative school in Belo Horizonte, Brazil. As a result of the project data literacy among participants increased, and the project initiated a sustained interest within the school community in using data to tell stories and create social change.
The growing number of tools for data novices are not designed with the goal of learning in mind. This paper proposes a set of pedagogical design principles for tool development to support data literacy learners. We document their use in the creation of three digital tools and activities that help learners build data literacy, showing design decisions driven by our pedagogy. Sketches students created during the activities reflect their adeptness with key data literacy skills. Based on early results, we suggest that tool designers and educators should orient their work from the outset around strong pedagogical principles.
As the field of K‐12 data science education continues to take form, humanistic approaches to teaching and learning about data are needed. Data feminism is an approach that draws on feminist scholarship and action to humanize data and contend with the relationships between data and power. In this review paper, we draw on principles from data feminism to review 42 different educational research and design approaches that engage youth with data, many of which are educational technology intensive and bear on future data‐intensive educational technology research and design projects. We describe how the projects engage students with examining power, challenging power, elevating emotion and lived experience, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labour visible. In doing so, we articulate ways that current data education initiatives involve youth in thinking about issues of justice and inclusion. These projects may offer examples of varying complexity for future work to contend with and, ideally, extend in order to further realize data feminism in K‐12 data science education. What is already known about this topic Data feminism is an emergent framework for changing data practices and discourse in service of equity and justice. Data science education is rapidly growing as a topic of interest in the educational technology research and design communities. Many educational technology and design projects have been launched and shared in publications that preceded the widespread distribution of the data feminism framework. What this paper adds Data feminism is partially re‐articulated in terms familiar to educational technology research communities. Prior and recent projects are organized with respect to how they illustrate potential connections to core data feminism principles. This paper identifies specific strategies that recent projects have used that have potential for realizing data feminism principles. Implications for practice and/or policy Educational technologists can use the re‐articulated principles of data feminism for education to inform their future design work. Tractable steps to achieve data justice that are attainable within existing educational systems can be pursued. Communities can and should bring together multiple ways of knowing to support new educational practices and futures with data.
We present the first full description of Media Cloud, an open source platform based on crawling hyperlink structure in operation for over 10 years, that for many uses will be the best way to collect data for studying the media ecosystem on the open web. We document the key choices behind what data Media Cloud collects and stores, how it processes and organizes these data, and its open API access as well as user-facing tools. We also highlight the strengths and limitations of the Media Cloud collection strategy compared to relevant alternatives. We give an overview two sample datasets generated using Media Cloud and discuss how researchers can use the platform to create their own datasets.
Against a backdrop of systemic disruption in the field of journalism writ large, data journalism represents a subarea that is undergoing rapid transformation due to the introduction of new tools and techniques as well as the changes in reporting practices as journalists and newsrooms experiment and innovate. This paper explores the challenges for data journalism educators to teach in such a rapidly shifting landscape. Drawing from our experiences teaching journalism students in higher education, we assert that the goal of data journalism education amidst this complexity is not to teach tech, nor even to teach technical skills, but rather to model for students strategies of dealing with transformation and complexity. These include peer learning, hands-on learning activities, modeling learning and information seeking, and establishing a culture of critique. We introduce a number of activities that put those approaches into practice, drawing on learning literature to support our fellow educators shifting from the "banking model" of education [10] to a learner-centered model [23]. Working with students to co-create knowledge, acting as a "Guide on the Side"[15] can help better prepare students for the constantly evolving ecosystem of technologies and tools that support data journalism.
Data ethics and fairness have emerged as important areas of research in recent years. However, much work in this area focuses on retroactively auditing and "mitigating bias" in existing, potentially flawed systems, without interrogating the deeper structural inequalities underlying them. There are not yet examples of how to apply feminist and participatory methodologies from the start, to conceptualize and design machine learning-based tools that center and aim to challenge power inequalities. Our work targets this more prospective goal. Guided by the framework of data feminism, we co-design datasets and machine learning models to support the efforts of activists who collect and monitor data about feminicide -gender-based killings of women and girls. We describe how intersectional feminist goals and participatory processes shaped each stage of our approach, from problem conceptualization to data collection to model evaluation. We highlight several methodological contributions, including 1) an iterative data collection and annotation process that targets model weaknesses and interrogates framing concepts (such as who is included/excluded in "feminicide"), 2) models that explicitly focus on intersectional identities rather than statistical majorities, and 3) a multi-step evaluation process -with quantitative, qualitative and participatory stepsfocused on context-specific relevance. We also distill insights and tensions that arise from bridging intersectional feminist goals with ML. These include reflections on how ML may challenge power, embrace pluralism, rethink binaries and consider context, as well as the inherent limitations of any technology-based solution to address durable structural inequalities.
We assert that visual-numeric literacy, indeed all data literacy, must take as its starting point that the human relations and impacts currently produced and reproduced through data are unequal. Likewise, white men remain overrepresented in data-related fields, even as other STEM (Science, Technology, Engineeering and Medicine) fields have managed to narrow their gender gap. To address these inequalities, we introduce teaching methods that are grounded in feminist theory, process, and design. Through three case studies, we examine what feminism may have to offer visualization literacy, with the goals of cultivating self-efficacy for women and underrepresented groups to work with data, and creating learning spaces where, as Philip et al. (2016) state, ‘groups influence, resist, and transform everyday and formal processes of power that impact their lives’.
Online video, a ubiquitous, visual, and highly shareable medium, is well-suited to crossing geographic, cultural,and linguistic barriers. Trending videos in particular,by virtue of reaching a large number of viewers in a short span of time, are powerful as both influencers and indicators of international communication flows. In this work, we study a large set of videos trending across 57 nations, collected from YouTube over a7-month period. We consider the set as a network of content flowing between nations, then develop conditional co-affiliation, a nation-nation co-affiliation index that enables a meaningful interpretation of network path length and the application of betweenness centrality. We observe a highly-interlinked network with remarkably similar co-affiliation levels between very different nations.However, Arabic-speaking nations appear more isolated, with the U.A.E. emerging as a key bridge. By analyzing video trend lifespans, we show that nations having many globally-popular video trends are reliably not the nation where those trends are strongest: we see no evidence to support the widely discussed idea of cultural exporter or trendsetter nations. We model correlations between co-affiliation and a selection of contextual factors. We note a surprisingly complex interaction between migration and shared video trends. Consistent with existing work on video popularity, we find that long trending times within one nation do not necessarily translate to reaching a wide global audience. This work expands on previous studies of the geographic popularity of videos by incorporating trending data and extending our analysis from video-nation affiliations to nation-nation co-affiliations. Characterizing these relationships is key to understanding the international cultural impact and potential of online video.
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