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…

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…

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…

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…

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…

In this study, we propose a MOOC Analytic Statistical Visual model (MOOC-ASV) to explore students' engagement in MOOC courses and predict their performance on the basis of their behaviors logged as big data in MOOC platforms. The model has multifunctions, which performs on visually analyzing learners' data by state-of-the-art techniques. The model…

The process of analyzing student data to determine an appropriate instructional decision is crucial for student academic growth. This article details how teachers can make data-driven decisions to carefully design writing instruction. Steps are presented for teachers to follow throughout the data driven decision-making process in order to meet…

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…

Educators globally are continually encouraged to use data to inform instructional improvement in schools, yet while there have been many recent innovations in data visualization and data science, educators are rarely included in dashboard co-design. On December 5 and 6, 2019, the Education Data Analytics Collaborative Workshop was held at Teachers…

The What Works Clearinghouse (WWC) at the Institute of Education Sciences reviews rigorous research on educational practices, policies, programs, and products with a goal of identifying 'what works' and making that information accessible to the public. One critique of the WWC is the need to more closely examine 'what works' for whom, in which…

Connecting teachers' perspectives with their practice is an enduring challenge shaping what and how we understand teaching. Researchers tend to bifurcate teachers' work between their private and their public lives. These "worlds" bring particular meanings that are rendered through the analyses of visual documentations of teaching and…

During the past decade, digital transformation enabled by big data and analytics emerged as a key theme in the business world. It promises to continue be a theme of major importance in the 2020s. Digital transformation comes at the cost of grappling with and analyzing the ever-growing volume of data. Data visualization techniques are seen as a…

This manuscript focuses on the role of data analytics in mediating how teachers make sense of racial and gender inequity in patterns of student participation in their classrooms. Five middle school mathematics teachers participated in a year-long professional development program, centered on data analytics generated by the EQUIP observation tool.…

In Technology Enhanced Learning field, learning analytics cover multiple research challenges, among which tracking data analysis and data indicator design and visualization. Part of our research effort is dedicated to changing their design process, in order to capitalize them. This would allow us to meet a need in cost savings of design workflow…

The "Forum Guide to Data Literacy" is designed to help education agencies understand and build data literacy skills among various stakeholder groups such as administrators, teachers, students, parents and other caregivers, school board members, legislators, and community groups. This resource defines and discusses the importance of data…