Data science encompasses the most prominent collection of methods for creating scientific knowledge in the 21st century. Currently, data scientists must navigate a wide-ranging and often incoherent ecosystem of tools, in addition to organizing sociotechnical interactions with colleagues across many fields of expertise. This predicament motivates…

This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency--cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable…

Artificial intelligence (AI) has been widely adopted in higher education. However, the current research on AI in higher education is limited lacking both breadth and depth. The present study fills the research gap by exploring faculty members' perception on teaching AI and data science related courses facilitated by an open experiential AI…

Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical…

Problem-based learning ignites hands-on exploration, ignites curiosity, and sparks lifelong problem-solvers. This Spotlight will help readers learn how virtual reality can be used for unique problem-solving and engagement; explore how students are publishing their research in peer-reviewed journals; uncover how after-school programs are helping…

To create opportunities for meaningful applications of data science for diverse students, we developed and implemented an online learning module focused on engaging students at a Hispanic Serving Institution (HSI) in an analysis of authentic soil data. Development of the module occurred over three design iterations involving interviews with 10…

Business analytics is a fast-growing field that requires a combination of technical, analytical, and communication skills. This article aims to identify the most sought after skills for business analytics jobs based on a content analysis of over 2600 online job postings. The results show that the top skills include analytics, communication,…

Data science education in Japan extends from elementary to high school students. However, some studies show that this has not enhanced interest or curiosity in data science. Therefore, gamification appears to be an efficient method for encouraging high school students' interest in data science, with research indicating that video games are…

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research…

The intersection of Data Science (DS) and Library and Information Science (LIS) is rapidly developing, with a notable need for ongoing transdisciplinary training between practitioners in these two fields. The LIS Education and Data Science Integrated Network Group (LEADING) fellowship program and its ancillary community of practice (CoP) showcase…

A professional practice is a type of internship, a practicum, that encompasses a supervised hands-on training experience for students to develop and identify the core and enabling competencies required in a professional environment. It also allows them to identify the developed and underdeveloped skills that are important in the labour environment…

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying…

Data analysis and interpretation has always played a fundamental role in the scientific curricula of high school students. The spread of digitalization has further increased the number of learning environments whereby this topic can be effectively taught: as a matter of fact, the ever-growing diffusion of data science across diverse sectors of…

Domestic and international competition for STEM talent is driving institutions across the research and innovation landscape to consider new policies and partnerships for building and managing STEM knowledge and skills. New levels of investment in human capital to increase U.S. innovation capacity and competitiveness will require coordination and…

As data science and instrumentation become key practices in common careers ranging from medicine to agriscience, chemistry as a core introductory course must introduce such topics to students early and at an accessible level. Advanced data acquisition and data science generally require expensive precision instrumentation and massive computation,…