DataEd 2023 Submissions

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Submission categories

The workshop will solicit three kinds of submissions:

Research & Research Proposal Papers on topics related to data systems education and training. They present new research ideas to be addressed with appropriate research methods, possibly presenting preliminary results.

Experience Reports describe learning and teaching experiences related to querying, data analysis, and data systems, ranging from after-school programs for children, to higher education, to end-user development communities, to workplace training of computing professionals. They interpret the experiences, highlight lessons learned, and formulate actionable advice, but do not need rigorous evaluation to support their claims.

Artefact Papers describe tools, technologies, datasets, or educational resources that support data systems education. Tool and resources papers should describe the development and use of the tool or resource and discuss its impact on the teaching and learning process. Educational resources can be lesson plans, education material, MOOCs, tutorials, assignment descriptions, etc. Papers on datasets should describe the methodology used to gather it and its provenance, along with its potential use and benefits for research and/or educational practice.

Contributions of all types should be up to 5 pages in length (excluding references, which have no page limit) in the ACM Proceedings Format, two-column (sigconf), with shorter submissions (2 pages in length) being encouraged. Reviewing will be single-anonymous, such that submissions include author names. Pending approval, all accepted papers will be published in the ACM Digital Library.

Important dates

All deadlines are 11:59 PM AoE.

Description Date
Paper submissions due March 1, 2023
Notification to authors April 6, 2023
Camera Ready due April 27, 2023
Workshop date June 23, 2023


Contributions are welcome in the broad area of data systems education: the teaching and learning of databases/data management/data systems topics, ranging across the whole field, from classical topics (such as physical design, query optimization, data modeling, data integration, visual analytics, and query languages) to contemporary topics (such as ML & AI for data management systems, data management for ML & AI, very large data science applications/pipelines, and responsible data management).

Topics of interest for contributions can involve, but are not limited to, one or more of:

  • teaching practices and instructional approaches supporting data systems education and training
  • education on data systems concepts in any context; e.g., higher education, non-CS majors, K-12, extra-curricular training, informal learning, lifelong and distance learning, professional training, on-the-job training, teachers’ professional development
  • learnability of query languages and tools
  • psychology of querying and data modeling
  • knowledge and skills requirements on data systems
  • data systems course and curriculum design
  • learning sciences work in the data systems content domain
  • integration of data management topics in other subjects in the curriculum (such as software engineering and ML & AI)
  • measurement instruments, evaluation, and assessment in data systems education
  • technology enhanced learning and intelligent tutoring in data systems
  • learning technologies and tools that support data systems education
  • ethical and responsible data management course and curriculum design
  • equity, diversity, inclusion in data systems education
  • any other aspects of the teaching and/or learning data systems topics.

Note that generic educational technology and applications of data systems for educational purposes are not in scope, except where they are used for data systems education.

Questions? Contact the organizers on