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This qualitative study explored the ways in which student use of media literacy tools and different media source materials can influence the conclusions students draw about race in the United States. The author visited eighth-grade social studies students in two different schools (one more economically advantaged; one more economically disadvantaged) for six visits apiece. During these visits, students engaged with several media sources (controlling for bias) covering two issues related to race (housing and immigration). The goal was to determine how the overall use of media literacy tools and the different types of source materials can influence the ways in which students view a difficult topic such as that of race in the United States. Students engaged in student-centered inquiry with these sources, and they shared their responses through discourse analysis featuring surveys, free-writes, small-group discussions, large-group discussions and individual interviews, all conducted in the classroom. The results of this study suggest that media literacy tools can have a profound impact on students’ analysis and critical thinking when they are engaging with media messages on tough topics such as race. As for the various media source materials explored, students responded most strongly to visual sources and to personal-experience sources about race. Hard-data sources, while not universally appealing to students, held significant influence among those who connected with telling the story of race in the U.S. through numbers. Students presented a clear desire to learn more about race, and to engage with more source materials in school to deepen their examination of this ever-challenging topic. They responded positively to the connection between the practices of media literacy and student-centered inquiry.

The purpose of this research study was to examine how teachers use pedagogical data literacy methods using the South Carolina Multi-Tiered System of Supports (SCMTSS) framework, and its influence on positive student outcomes in reading achievement for all students and students receiving reading intervention supports in Kindergarten through third grade. Teachers' pedagogical data literacy, or the intersection of educators' knowledge and skills to transform the data into instructional strategies to meet students' needs, provides a lens to fulfill the SCMTSS framework approach of using data-based problem solving to maximize growth of all students. This research study identified SCMTSS Tier I that needed revisions to strengthen the instructional effectiveness at Excellent Elementary School. The South Carolina Department of Education identifies three tiers of the SCMTSS framework necessary for successful implementation to result in positive student outcomes, including Tier I, Tier II, and Tier III instruction and interventions. The SCMTSS framework guides educators in identifying and addressing academic difficulties by monitoring student responses to target interventions at different tiers of instruction and providing data to direct the intervention process. The goal was to improve achievement using research-based interventions that correlate to students' levels and instructional needs. After four years of implementing the tiered SCMTSS framework that provides early reading intervention to students who struggle, 78% of students at Excellent Elementary School needed support after the first year of reading intervention. This study utilized a concurrent nested mixed methods approach using multiple types of instruments and approaches to data analysis through the use of the Plan-Do-Study-Act inquiry method of improvement science. Understanding variation in performance is an integral principle of improvement science. The vitality of improvement science lies with organizations understanding how to replicate successful results in varied situations. Ultimately, there needs to be transferability of the initiatives and interventions for continuous improvement to broadcast for the betterment of educational systems. This study informed efforts to increase students' literacy for all students and reduce the number of students who need continued reading support services for more than one school year. Keywords: Office of Early Learning and Literacy, pedagogical data literacy, Response to Intervention, South Carolina Multi-Tiered System of Supports, socioeconomic status, South Carolina Department of Education

This dissertation examines the efficacy of a professional development program in developing data literate and culturally proficient community college instructors. Existing research suggests that there is a need for high-quality professional development in Cultural Proficiency and data literacy for community college instructors to make continuous improvements in the classroom. As such, the Regional Consortium for the San Diego and Imperial Counties Community Colleges executed the inaugural Strong Workforce Faculty Institute in 2020—a professional development program aimed at 1) fostering strong collaboration and engagement among faculty, researchers, and deans; 2) encouraging self-reflection, inquiry, and research-based decision-making; and 3) inspiring teaching and learning from a culturally inclusive perspective. The first cohort began with 245 full-time and adjunct instructors in February 2020 and ended with 223 program completers in September 2020—a 91 percent completion rate. This study examines the conditions necessary for a professional development program to effectively train instructors to use student outcomes data for continuous improvements in culturally diverse classrooms and assesses to what extent the program met its goals and contributed to participants’ learning. Using mixed-methods research, this study analyzes quantitative data from survey responses and supplements the findings with qualitative information from interviews with participants. The findings will be used to improve future iterations of the Strong Workforce Faculty Institute specifically and to inform community college instructor professional development more broadly.

This study applies the knowledge, skills, and abilities (KSA) framework for eScience professionals to data service positions in academic libraries. Understanding the KSAs needed to provide data services is of crucial concern. The current study looks at KSAs of data professionals working in the United States academic libraries. An exploratory sequential mixed method design was adopted to discover the KSAs. The study was divided into two phases, a qualitative content analysis of 260 job advertisements for data professionals for Phase 1, and distribution of a self-administered online survey to data professionals working in academic libraries research data services (RDS) for Phase 2. The discovery of the KSAs from the content analysis of 260 job ads and the survey results from 167 data professionals were analyzed separately, and then Spearman rank order correlation was conducted in order to triangulate the data and compare results. The results from the study provide evidence on what hiring managers seek through job advertisements in terms of KSAs and which KSAs data professionals find to be important for working in RDS. The Spearman rank order correlation found strong agreement between job advertisement KSAs and data professionals perceptions of the KSAs.

In many US homes, 15% of the energy that can be saved is hidden beneath complex mathematical calculations. Hidden energy savings can be revealed by converting mathematical calculations to data visualizations, creating a story for residents to see how they are consuming energy. Cloud-based data visualization platforms offer the ability to appropriately communicate complex building energy data to a broad set of stakeholders. Unfortunately, proprietary solutions are too expensive and open-source options lack standardization for cloud-based energy monitoring. This study aims to create a comprehensive energy dashboard display to increase residents' energy awareness of how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight visualization chart types that are used on utility bills and energy monitoring devices. In addition, the literature review uncovered eight additional visualization chart types that have the functionality to visualize energy data. Next, the visualization chart types were combined with data modeling design techniques to create prototype energy dashboard displays to communicate energy insights to residents. Soon utility companies will begin to provide data visualizations for the majority of their customers. The insights from this study can help to inform and lead the development of commercially used data visualizations. In addition, this research can provide utility companies with a blueprint on how to share energy consumption data with customers.

The notion of using data to inform educational decisions is not novel; in fact, teachers and school leaders have used data in various forms and for a variety of reasons for decades. However, challenges remain with the timely availability of data, accessibility to the data, and teachers’ understanding of how these data can be transformed into action that impacts instructional decisions. Without appropriate professional development, support, and leadership, teachers may struggle to use data to make sound decisions and take action in their classrooms. This quantitative correlation study examined elementary school teachers’ levels of readiness regarding data-driven decision-making (DDDM) practices and school performance in mathematics and ELA proficiency. The data were collected using the Statewide Data-Driven Readiness Study Teacher Survey authored by McLeod and Seashore (2006). Readiness levels were calculated from teacher responses in four areas: assessment, acting upon data, school support systems, and school culture. School proficiency levels were collected from the 2018–2019 New Jersey School Performance Report in ELA and mathematics. The results indicated that significant relationships existed between K–5 elementary teachers’ self-reported readiness on overall DDDM practices and high student achievement levels in ELA. No significant relationships were discovered between teachers’ DDDM readiness levels and mathematics achievement. This study adds to the existing body of research on DDDM practices and supports the foundational work of previous studies and theoretical frameworks on DDDM. Developing teachers’ capacity for DDDM is an essential part of effective practice. Improving data literacy must be a priority for school leaders to improve instructional practice and increase students’ learning outcomes.

Significant resources have been directed towards K-12 computing and data education over the past ten years, as part of what has come to be known as the CSforAll initiative. This initiative has focused on raising awareness of computing education among parents and students, developing situated learning progressions that resonate with many different interests and pursuits, training teachers, and addressing issues of underrepresentation in computing among females and racial minorities. In this dissertation, I argue that as the CSforAll initiative continues to expand, it is important for the education community to also reflect on the forms of knowledge that are believed to be essential, and the presumed benefits of computing and data education. Specifically, how might the goal of producing citizens with robust computing and data literacies change what is considered to be fundamental to a computing education; as well as the kinds of contexts in which computing and data science are situated?I use the term sociotechnical literacy to name this vision for computing education, which I define as a broad set of social and technical practices, strategies, ideas, and dispositions that can help people to reason about the computer-mediated systems that shape their everyday lives. As the term suggests, I argue that it is important for learners to engage with technical ideas as well as their social applications and implications. To examine what this might mean for teaching and learning, I describe two design experiments that I conducted with young people (ages 14 – 22). Each approach aimed to make the applications of computing primary (rather than treating applications as the backdrop from which the abstractions of computation are motivated), so that learners could examine some of the specific ways in which data and computing might be directed to particular goals, subject to real possibilities and constraints, and in relation to alternative forms of participation. I examine the possibilities and limitations of each approach. I also analyze some of the assumptions that framed the design experiments – which were naïve, but also reflective of a broader ethos that pervades CSforAll. I reflect on what these studies collectively reveal about the possibilities, limitations, and risks of data and computing, as situated in the lives of young people; as well as what this might mean for helping young people develop a robust sociotechnical literacy. There are very real limits to what can be accomplished with computing and data alone. There are also significant benefits and risks associated with the many sociotechnical systems that shape our lives. As such, I argue that rather than positioning computing education as a remedy to various social ills, we instead offer young people a fair explanation of what computing is and is not capable of, grounded within specific contexts involving real people. I conclude with what this fair explanation might include, and how it might be fostered.