Theory and Research in Data Science Education

Dear Colleagues,

The unprecedented growth in the availability and accessibility of data, highly influenced by the increasing ubiquity of digital media and the Internet (Mund, 2022), has opened new possibilities to develop greater understandings of every aspect of human existence. Data science, “the study of extracting value from data” (Wing, 2019), has become indispensable for approaching society’s most pressing societal problems (e.g., climate change, health, social justice) (Tanaka et al., 2022). However, while open and multisource access to information is a key value of modern democratic societies, a lack of readiness for navigation in the dynamic information landscape can become a threat to the common good (Bobrowicz et al., 2022). The ways in which universal access to information can backfire without citizens’ readiness for responsible, well-reasoned choices (Bobrowicz et al., 2022) have rarely been so painfully clear than during the COVID-19 global pandemic, which required everyone to make sense of data for community spread, levels of risk, and vaccine efficacy.

The ever-increasing growth of data has increased the demand for a next generation of data scientists that can anticipate user needs and develop optimal solutions to address business, academic, and societal challenges (Seshaiyer & McNeely, 2022). More importantly, basic data science skills are becoming increasingly important for any profession, from technology, science, finance, journalism and politics to art and history (Mund, 2022), as well as for active and responsible citizenship.

Data Science is one of the fastest-growing fields of study at the collegiate level. Recently, there has also been a call for data science to be included in school curricula (Lee et al., 2022). Responding to this call, data science education has been recently established as a new field of educational research and practice that aims to build students’ data science literacy starting from the early years of schooling. This Special Issue aims to provide a forum for the sharing of research findings, ideas, and perspectives on this new but fast-growing field of inquiry. Recommended topics for the Special Issue include but are not restricted to the following:

• Essential concepts and core ideas fundamental to data science literacy;
• Teaching and learning of key data science concepts and practices (e.g., exploration of messy data, data cleaning and wrangling, use of coding) at the K-12, undergraduate, graduate, and professional levels;
• Pedagogical models and instructional approaches underlying data science education within and across disciplines;
• Data Science as a bridge between individual disciplines and STEM/STEAM education;
• Building connections between data science education and data science applications in industry;
• Data science literacy for all: equity, inclusion, accessibility, and diversity;
• Capacity development of data-science literate educators and trainers;
• Use of technological tools (e.g., Jupyter Notebooks, Python) to support the teaching and learning of data science;
• Data science and data analytics as tools for enhancing the educational process;
• Ethics in data science and the role of education;
• Data science literacy as a tool for civic engagement and social justice;
• Use of AI technologies in Data Science (school) projects (either as learning tools, or as a tool for teachers’ design).

The articles should report on original empirical studies, which will demonstrate validated practical experiences related to the teaching and learning of data science. The Research Topic will also include conceptual essays contributing to future research and theory building by presenting reflective or theoretical analyses, epistemological studies, integrative and critical literature reviews, or the forecasting of emerging trends in data science education.

Prof. Dr. Maria Meletiou-Mavrotheris
Prof. Dr. Aisling Leavy
Prof. Dr. Daniel Frischemeier
Dr. Sibel Kazak
Dr. Efi Paparistodemou
Dr. Dionysia Bakogianni
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data science
• data science education
• data science literacy
• data analytics
• statistics
• statistics education
• statistics literacy
• computational thinking
• machine learning