Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed…

Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that…

This study focuses on describing data based school management processes in Türkiye, Spain and Romania. The study group consists of 49 participants. Maximal variation and stratified sampling were applied. For internal trustworthiness, respondent validation, data triangulation and cross check were utilized. For transferability, 'expert opinions' and…

When we imagine the work of a data analyst, we often picture meaningful data analysis and beautiful data visualizations. Although that is an exciting part of the job, data analysts actually spend the majority of their time acquiring, cleaning, and preparing data for analysis. This teaching case guides students through some of the most common data…

Despite vital professional development support through the National Art Education Association's Data Visualization Working Group and other scholars' engagement with the topic in the literature, pedagogical literature that connects contemporary data visualization methods to art teaching practices is limited. While acknowledging that many of us have…

The increasing availability of digital tools in science classrooms can provide students with more frequent and easier access to large amounts of data. Large data sets have considerable epistemological potential, as they enable, for instance, the observation of otherwise unobservable phenomena, but it must be assumed that handling them places…

Background: New technology and increased collaboration have revolutionized how scientists work with data. This creates a need to identify new aspects of working with scientific data that are important for K-12 students to learn. Purpose: To address this need, we conducted a study with practicing scientists and K-12 science teachers. The purpose of…

How can we prepare students for a world where data-driven decision-making shapes nearly every aspect of life? "Data Minds: How Today's Teachers Can Prepare Students for Tomorrow's World" helps K-8 educators infuse data literacy into everyday lessons across disciplines, without overwhelming existing curricula. Data literacy is an ability…

We are surrounded by data and data visualizations in our everyday lives. To help ensure that students can critically evaluate data--and use it to promote social justice--this book outlines principles and practices for teaching data literacy as part of social studies education. The author shows how social studies content and skills can enhance both…

Purpose: This paper aims to consider ways to visually model data generated by qualitative case studies, pointing out a need for visualizations that depict both synchronic relations across representations and how those relations change diachronically. To develop an appropriate modeling approach, the paper critically examines Max Boisot's I-Space…

This paper offers a creative methodology for co-creating and analysing data when conducting research with children, families and practitioners in the early years. Instead of relying heavily on either adult observation or language when, for example, interviewing children, we propose the method of choreographed visual montage. We show the novelty…

Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to…

This manuscript describes the use of real data applied to a fictional real-estate firm for teaching data visualization to university students. In the case study, students employ data analytic techniques in Tableau to clean, organize, and analyze real estate data. By creating visualizations, students address several questions about how selling…

Learning performance data, such as correct or incorrect answers and problem-solving attempts in intelligent tutoring systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80%90% missing observations) in most real-world applications. This data…

Students now have readily available and powerful tools to access, manipulate, combine, and visualize data. Acquiring data and visual literacy requires more than knowledge of how to use these tools. Students need to engage with assignments that challenge them to make relatively complex visualizations, interpret them, and explain why these…