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To meet the challenges of a data-driven society, high school students need new arrays of literacy skills. In the United States, school librarians, who work across disciplines, are well-positioned to help students improve their data practice, but they first need new domain knowledge. This article presents findings from an evaluating survey and session evaluation data from a virtual data literacy conference, which were part of a federally-funded project to develop data literacy skills among high school librarians and educators. Findings indicated a noticeable shift in participant perceptions of the need and urgency for data literacy instruction across content areas and grade levels concurrent with implementation of content-area data literacy standards. While the conference was geared toward high school educators and librarians, participants represented a broad audience of K-12 educators and K-20 librarians. The findings provide a valuable snapshot of shifting educational standards and priorities, along with needed pedagogical support and resources. [ABSTRACT FROM AUTHOR]

How do we teach and learn with our students about data literacy, at the same time as Biesta (2015) calls for an emphasis on 'subjectification' i.e. 'the coming into presence of unique individual beings'? (Good Education in an Age of Measurement: Ethics, Politics, Democracy. Routledge) Our response to these challenges and the datafication of higher education, is a hands-on approach to building an open, collaborative pedagogy of data literacy, based on Bayesian Networks (BNs) (Pearl, J. 1985. Bayesian Networks: A Model of Self–Activated Memory for Evidential Reasoning. Los Angeles: University of California (Computer Science Department)). BNs can be used to merge subjective views of the learning process with objective data analysis from the learning environment; BNs are visual data constructs and, unlike other Machine Learning approaches that obfuscate and complexify, BNs can be developed to reveal relationships from observations. In this paper, we share ways in which teachers and students can work together in a praxis approach to use data to 'read the world' around them (Freire, P. 1970. Pedagogy of the Oppressed. New York: Continuum. 125). [ABSTRACT FROM AUTHOR]

As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the "Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action." This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users., (©Jesse Yedinak, Maxwell S Krieger, Raynald Joseph, Stacey Levin, Sarah Edwards, Dennis A Bailer, Jonathan Goyer, Colleen Daley Ndoye, Cathy Schultz, Jennifer Koziol, Rachael Elmaleh, Benjamin D Hallowell, Todd Hampson, Ellen Duong, Abdullah Shihipar, William C Goedel, Brandon DL Marshall. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 12.02.2024.)

In the information age, a person’s ability to read and make data visualizations is nearly as important as being able to read and write text. This article reports the results of a multi-phase study conducted in informal learning environments in three US science museums. The goal of the study was to determine the familiarity of youth and adult museum visitors with different visualization types. To address this, a total of 273 visitors were shown 5 out of 20 different visualizations that included two charts, five maps, eight graphs, and five network layouts. They were asked to judge the familiarity of the visualization, provide information on how to read it, and provide a name and identify typical locations where they would encounter the data display and possible data sources that might be visualized in this way. The results show that while most participants have a strong interest in science, math, and art, many have a hard time naming and interpreting visualizations. Participants in this study commonly encounter visualizations in school, in books, at work, on the Internet, and in the news. Overall, they were more familiar with basic charts, maps, and graphs, but very few are familiar with network layouts and most have no ability in reading network visualizations. When asked how they would interpret the visualizations, most participants pointed to superficial features such as color, lines, or text as important to developing understanding. Overall, we found that participants were interested in the visualizations we presented to them, but had significant limitations in identifying and understanding them. The results substantiate intuitions shared by many regarding the rather low level of data visualization literacy of general audiences. We hope they will help catalyze novel research on the development of easy-to-use yet effective visualizations with standardized names and guaranteed properties that can be readily used by those interested to understand and solve real-world problems. The results also have implications for how information visualizations are taught and used in formal and informal education, the media, or in different professions. [ABSTRACT FROM AUTHOR]

Purpose - This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education. Design/methodology/approach - We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4 Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4 Vs of Big Data - volume, velocity, variety, and veracity - help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL "as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting" (p. 1, in press). Findings - Using the guiding frameworks, we identified four educational data crossroads as follows: Crossroad 1: Unintended Increase in Workload Volume; Crossroad 2: Unrealistic Expectations of Data Velocity; Crossroad 3: Data Variety in Silos; and Crossroad 4; Data Veracity and Policy Agenda Mismatch. In this paper, we explain each of these crossroads in more detail with some examples. Originality/value of the paper - Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education. [ABSTRACT FROM AUTHOR]

Science education in the United States should shift to incorporate innovative technologies and curricula that prepare students in the competencies needed for success in science, technology, engineering, and math (STEM) careers. Here we employ a qualitative case study analysis to investigate the system variables that supported or impeded one such reform effort aimed at improving elementary students' science learning. We found that, while some program design features contributed to the success of the program (i.e., a strong multi-institutional partnership and a focus on teacher training and instructional supports), other features posed barriers to the long-term system-level change needed for reform (i.e., low levels of social capital activation, low prioritization of science learning, and frequent turnover of key personnel). In light of these findings, we discuss broader implications for building the capacity to overcome system barriers. In this way, an in-depth examination of the context-specific barriers to reform in this educational system can inform efforts for future reform and innovation design. [ABSTRACT FROM AUTHOR]

Nurses in the 21st-century are expected to be data and information literate and proficient in data management. Nurses graduating from baccalaureate programs must be able to use computers and information systems and apply data and evidence to inform practice. Those competencies are also essential for the entire nursing workforce. That puts the onus on school nurses, school nurse supervisors, school districts, and state affiliates to take responsibility for comprehensive data and information literacy professional development. Fortunately, the National Association of School Nurses (NASN) has anticipated the needs of the membership. NASN included data and information capacity building as a part of The National School Health Data Set: Every Student Counts!, a national standardized data set and data collection initiative.