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Data Science Academy

ADAPT Course Model

The Data Science Academy designs and delivers instruction through the ADAPT model: All-campus Data science through Accessible Project-based Teaching and learning model. Our courses are open to all learners and have included undergraduates, graduate students, faculty, staff, alumni and community members.

All-campus Data science through Accessible Project-based Teaching and learning model has three components: Project-based Learning, 10 Common Learning Elements, and Identity-conscious Choices
The ADAPT model has three components: Project-based Learning, 10 Common Learning Elements, and Identity-conscious Choices.

Project-based Learning

Our courses have homework and projects, but no quizzes, tests or exams.  Students complete projects, sometimes as a series of assignments or as a final project.  Projects are hands-on, workforce-relevant ways to engage and retain ideas and skills.

10 Common Learning Elements

Our courses are designed to have some elements in common, which are fundamental to data science.  They include:

Data Perspectives

  1. Recognizing data as information – not truth – with error, variability, and degrees of inclusion/exclusion,
  2. Explaining what it means to be a data scientist and data-enabled,
  3. Observing a diverse collection of data scientist role models and careers,

Data Practices

  1. Examining how data are created, and the related assumptions and collection practices,
  2. Practicing data curation, wrangling, and cleaning,
  3. Assessing validity of data, methods, results, and communication,
  4. Employing accessibility practices,
  5. Investigating ethical issues and ways to approach them,

Data Discoveries

  1. Articulating current issues or open questions in data science, and
  2. Specifying exciting discoveries or impacts of data science.

Identity-conscious Choices

We prioritize learning experiences in which students have the agency to make genuine choices. Making decisions about which data to consider, which tools to use and which questions to explore give students opportunities to develop their identity as data-empowered problem solvers.

ADAPT Data Science Education Research

The Data Science Academy has a robust educational research program with three funded research projects in progress:

  1. NC State Data Science Education Postdoctoral Fellowship funded by NSF
  2. NSF IUSE – Creating Diverse Data Science Learning Pathways
    • Research Leads:  Sunghwan Byun, Shiyan Jiang, Rachel Levy
  3. Data Explorers – K-12 Student and Teacher Education funded by COMAP
    • Research Leads: Ruby Ellis
  4. NC Department of Health and Human Services Data at Work Course Pilot
    • Research Leads: Gemma Mojica, Rachel Levy

Want to conduct Education Research with DSA and the ADAPT model?

The Data Science Academy welcomes partners who would like to try the ADAPT model in their courses or curriculum development.  To participate, contact datascienceacademy@ncsu.edu, attn: Dr. Levy.  

The Data Science Academy also collects examples of student work (IRB# 24315) for research purposes.  If you would like to apply for permission to use DSA data in your research please submit this form at least 3 months in advance:  DSA Ed Research Application.docx Please email questions to datascienceacademy@ncsu.edu, attn: Dr. Byun.