Data Literacy in STEM Education
A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "STEM Education".
Deadline for manuscript submissions: 30 May 2026 | Viewed by 98
Special Issue Editor
Interests: cognitive psychology; junior high/intermediate/middle school education and teaching; mathematics teacher education; secondary education and teaching; technology integration
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
There is a global consensus that data literacy is a foundational skill for the 21st century, as well as a prerequisite for informed citizenship. It is also acknowledged that improving students’ ability to read, understand, analyze, and infer educational conclusions from and about data needs to become a priority in curriculum and instruction. Despite these strong endorsements, there is evidence that current efforts to meet the goal of improving data literacy skills across K-12 grades have been less than successful. Some of the primary barriers to systemic and widespread implementation have been associated with an absence of coherent curricular models, a lack of sustained professional development opportunities focused on improving teachers’ own data literacy, and limited research-based resources that can allow us to track the growth of learners, relying on learning trajectories, as well as case studies of various methodologies and tools used to advance students’ data literacy skills in disciplinary contexts.
For this Special Issue, we invite international scholars to share contributions that examine models, methods, and approaches used for advancing the integration of data literacy across STEM education disciplines. Topics of interest include (but are not limited to) the following:
- Assessing students’ data literacy: Rubrics, tasks, formative assessments, and validated instruments designed for measuring learners’ data literacy in STEM education;
- Curriculum design: Curriculum materials developed for use in K-12 settings;
- Use of digital tools and platforms for data collection, analysis, and visualization (e.g., spreadsheets, Python, Jupyter Notebooks, CODAP);
- Case studies of outcomes of the use of digital tools and platforms focused on enhancing students’ data literacy;
- Platforms used or suggested for engaging students with authentic datasets and studies that have focused on exploring their impact;
- Research reports on models for preparing pre-service and in-service teachers to teach data literacy in STEM;
- Case studies of successful teacher PD programs in data literacy in STEM;
- Theoretical and empirical studies that focus on the integration of AI and ML in data literacy in STEM.
Prof. Dr. Azita Manouchehri
Guest Editor
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 250 words) can be sent to the Editorial Office for assessment.
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.
Keywords
- data literacy
- data modeling
- teaching and learning
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