Improving Equity in Data Science: Re-Imagining the Teaching and Learning of Data in K-16 Classrooms

Improving Equity in Data Science offers a comprehensive look at how data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. It provides examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics.

Format: Paperback / softback
Length: 190 pages
Publication date: 03 June 2024
Publisher: Taylor & Francis Ltd

Improving Equity in Data Science: A Comprehensive Look at K-16 Classroom Settings
Data science has the potential to be a powerful tool for promoting equity and empowerment in various fields, including education. However, achieving equity in data science requires a comprehensive approach that goes beyond simply broadening participation in educational opportunities. In the book "Improving Equity in Data Science," authors explore the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting.

One of the key themes of the book is the need for field-wide definitions, literacies, and practices for data science teaching and learning. The authors argue that these definitions, literacies, and practices should be commonly discussed and used to ensure that data science is accessible and inclusive to all students. To support this argument, the book provides examples from research of these practices and literacies in action.

Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work.

Practitioners and teacher educators will be able to answer the question, "How can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?" whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms.

One of the challenges that data science faces in achieving equity is the lack of diversity in the field. Data science is often dominated by white males, which can lead to biases and discrimination in the development and application of data science technologies. The book highlights the importance of promoting diversity and inclusion in data science and provides strategies for doing so.

Another challenge is the lack of access to data science learning opportunities. Data science requires specialized equipment and software, which can be expensive and inaccessible to many students. The book provides examples of how data science can be made more accessible to students through community-based programs, online courses, and partnerships with local businesses.

In addition to these challenges, data science also has the potential to be used for harmful purposes. For example, data science can be used to track and monitor individuals, which can lead to discrimination and surveillance. The book provides strategies for addressing these ethical concerns and promoting responsible data science practices.

To address these challenges, the book offers a range of solutions. One of the solutions is to promote diversity and inclusion in data science by providing training and education to underrepresented groups. The book also provides strategies for making data science learning opportunities more accessible to students through community-based programs, online courses, and partnerships with local businesses.

Another solution is to promote responsible data science practices by providing guidelines and regulations for the use of data science technologies. The book provides examples of how these guidelines and regulations can be implemented in various settings, such as schools and businesses.

In conclusion, "Improving Equity in Data Science" offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting. The book makes the case for field-wide definitions, literacies, and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. By promoting diversity and inclusion, making data science learning opportunities more accessible, and promoting responsible data science practices, we can help to ensure that data science is a force for good in society.

Weight: 310g
Dimension: 152 x 229 x 16 (mm)
ISBN-13: 9781032428628