Data Moves

Erickson, Tim; Wilkerson, Michelle Hoda; Finzer, William; Reichsman, Frieda

doi:10.5070/t5121038001

Cited by 33 publications

(16 citation statements)

References 11 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This includes what are often referred to as disciplinary practices-the approaches, methods, and instruments developed within science and statistics communities to generate and analyze data sets (Bybee, 2011). It also includes computational methods and tools emerging from the data science community, which shape what analyses are possible and accessible to young learners (Erickson et al, 2019;Konold, 2007;McNamara, 2018). Finally, the cultural layer includes the norms and procedures that might be developed through classroom consensus as a student community negotiates collective approaches to data generation and analysis (Manz, 2016), as well as students' own repertoires of cultural practices and knowledge, which can serve to inform what they choose to attend to when engaging in reasoning about data (González et al, 2006).…”

Section: Cultural Layermentioning

confidence: 99%

A Call for a Humanistic Stance Toward K–12 Data Science Education

Lee

Wilkerson

Lanouette

2021

Educational Researcher

View full text Add to dashboard Cite

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.

show abstract

Section: Cultural Layermentioning

confidence: 99%

A Call for a Humanistic Stance Toward K–12 Data Science Education

Lee

Wilkerson

Lanouette

2021

Educational Researcher

View full text Add to dashboard Cite

show abstract

“…Those decisions are associated with forms of power and inherent bias. Categories for gender in existing datasets, such as what may be downloaded from government websites, are another space where thoughtful examination for what is being represented and erased in the process of datafication might occur (Erickson et al, 2019). Developing an understanding of the affordances and limitations of datafication by examining categories in this way complexifies conceptions of data and moves towards a more humanistic stance towards data science education (Lee, Wilkerson, et al, 2021).…”

Section: Examine Labels Categories Rankings and Claims About Authoritymentioning

confidence: 99%

Taking data feminism to school: A synthesis and review of pre‐collegiate data science education projects

Lee

Pimentel

Bhargava

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

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.

show abstract

“…Meanwhile, the intersection of technology with data literacy and the arts can support traditional practices in data science (eg, data moves, Erickson et al, 2019) and the arts (eg, creative expression), qualitative data production (eg, as photos); data re‐visualization (eg, sculpture); audience engagement (eg, through dissemination and evocative aesthetics; [Brinch, 2020; Lupi & Posavec, 2016]); cross‐subject learning and instruction (eg, by making participation more accessible); and assessment (eg, by making students' ideas visible).…”

Section: Background and Conceptual Frameworkmentioning

confidence: 99%

Tensions and synergies in arts‐integrated data literacy instruction: Reflections on four classroom implementations

Matuk

DesPortes

Amato

et al. 2022

Brit J Educational Tech

View full text Add to dashboard Cite

Data‐art inquiry is an arts‐integrated approach to data literacy learning that reflects the multidisciplinary nature of data literacy not often taught in school contexts. By layering critical reflection over conventional data inquiry processes, and by supporting creative expression about data, data‐art inquiry can support students' informal inference‐making by revealing the role of context in shaping the meaning of data, and encouraging consideration of the personal and social relevance of data. Data‐art inquiry additionally creates alternative entry points into data literacy by building on learners' non‐STEM interests. Supported by technology, it can provide accessible tools for students to reflect on and communicate about data in ways that can impact broader audiences. However, data‐art inquiry instruction faces many barriers to classroom implementation, particularly given the tendency for schools to structure learning with disciplinary silos, and to unequally prioritize mathematics and the arts. To explore the potential of data‐art inquiry in classroom contexts, we partnered with arts and mathematics teachers to co‐design and implement data‐art inquiry units. We implemented the units in four school contexts that differed in terms of the student population served, their curriculum priorities, and their technology infrastructure. We reflect on participant interviews, written reflections, and classroom data, to identify synergies and tensions between data literacy, technology, and the arts. Our findings highlight how contexts of implementation shape the possibilities and limitations for data‐art inquiry learning. To take full advantage of the potential for data‐art inquiry, curriculum design should account for and build on the opportunities and constraints of classroom contexts. What is already known about this topic Arts‐integrated instruction has underexplored potential for promoting students' data literacy, including their appreciation for the role of context and real‐world implications of data and for the personal and social relevance of data. Arts‐integrated instruction is difficult to implement in school contexts that are constrained by disciplinary silos. What this paper adds Descriptions of four data‐art inquiry units, which take an arts‐integrated approach to data literacy. Examples of the synergies and tensions observed between data literacy, technology, and the arts during classroom implementation in four different schools. Reflections on the role of school contexts in shaping disciplinary synergies and tensions. Implications for practice and/or policy Arts‐integration offers opportunities for data literacy learning. Consideration of the unique resources and constraints of classroom contexts is critical for fulfilling the promises of data‐art inquiry learning. There is a need to develop school support specific to arts‐integrated data literacy instruction.

show abstract

Data Moves

Cited by 33 publications

References 11 publications

A Call for a Humanistic Stance Toward K–12 Data Science Education

A Call for a Humanistic Stance Toward K–12 Data Science Education

Taking data feminism to school: A synthesis and review of pre‐collegiate data science education projects

Tensions and synergies in arts‐integrated data literacy instruction: Reflections on four classroom implementations

Contact Info

Product

Resources

About