Coeducation in Digital Competence and Data Science: A Conceptual Alignment Study
1 Department of Didactics of Mathematics, University of Granada, 18071 Granada, Spain
2 Department of Mathematics, IES José Luis López Aranguren, 28945 Fuenlabrada, Madrid, Spain
*Correspondence: jmcontreras@ugr.es (José Miguel Contreras García)
Abstract
In the European context, promoting digital competence across society has had a widespread impact at all levels of the education system, leading to multiple regulatory developments according to their scope: citizens’ digital competence, teachers’ digital competence, and the digital competence of educational organizations. Likewise, Data Science has gained prominence in higher education and research and is now commonly described as the fourth paradigm of science. This conceptual paper examines the connections between citizens’ digital competence and Data Science Education. To do so, it draws on the DigComp 2.2 framework for citizens’ digital competence, the Reference Framework for Teachers’ Digital Competence (MRCDD, from its Spanish acronym), and the data cycle, a theoretical lens frequently adopted in educational research on Data Science. Beyond the fact that data constitute a core component of digital competence and that Data Science demands an advanced development of this competence, we identify a strong alignment between the digital-competence reference frameworks and the theoretical framework underpinning Data Science Education. In addition, we pinpoint an area for improvement in order to achieve comprehensive preparation in both domains, emphasizing the need to incorporate aspects related to environmental sustainability, health, and well-being. Framed through the lens of Whole Schooling, these connections underscore the need for coordinated, schoolwide efforts toward equitable digital and data education, including coeducational strategies that support gender equity in Data Science learning. In addition, we outline equity-by-design implementation principles, addressing accessibility, differentiated pathways, and digital-divide mitigation, so that integrated digital competence and Data Science learning opportunities are realistically available to all students, including those with disabilities, multilingual backgrounds, and socio-economically disadvantaged contexts. Finally, we propose that compulsory education integrate this training to strengthen citizens’ digital competence while also providing an initial pathway toward the more specialized technological profiles that society demands across all areas of knowledge.
Keywords
- digital transformation
- digital competence
- data science education
- data cycle
- educational research
