Data Science | K-10

BIG IDEAS

A Selection of Big Ideas from Data Science – from GAISE II (2021).

Formulate statistical investigative questions

  • Formulate statistical investigative questions
  • Students generate ideas and ask questions – creating and refining statistical investigation questions

Collect/consider data

  • Students learn what counts as data (eg visuals, sounds, numbers, categories) and understand that people collect data to answer questions
  • Students develop strategies to collect and organize data of various types and from various sources
  • Students design studies to answer statistical investigative questions

Analyze data

  • Students develop ways to represent and interrogate data to notice, describe and analyze patterns
  • Students recognize variability and use technology to develop models that incorporate statistical measures

Interpret and communicate

  • Students decide key results to include in a data report that answers the statistical investigative question
  • Students communicate their results through, for example, a data visual, a poster, a video, a data story
  • Students explore and share explanations, paying careful attention to what conclusions the data supports. They consider which alternatives are reasonable given the variability in findings

What are Data Science K-10 Big Ideas?

Data Science K-10 Big Ideas are descriptions of the most important content in data science through the grades to help focus attention on ways to increase data literacy. Big ideas are those that are central to the discipline of data science, and that link understandings into a coherent whole. (Read Overview)

How do I use these materials?

Teaching to big ideas in data science means choosing tasks and data talks that give students’ experience inside the big ideas. When teaching to big ideas many different content areas are usually met, as the tasks offer rich learning experiences. (Read Overview)

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Grade K

Big Ideas

Formulate statistical investigative questions

  • Develop curiosity through noticing and wondering about data rich situations.
  • Generate ideas and ask questions. The teacher helps refine, direct and create statistical investigative questions.

Collect/consider the data

  • Consider: What is data? Understand that people collect data to answer questions and that data can vary (eg objects have different colors or sizes).
  • Develop strategies to collect and organize data – eg. sort collections of objects into categories that they have chosen.

Analyze

  • Develop ways to represent data eg with tally marks, or as pictures or a drawing.
  • Notice, describe and analyze patterns.
  • Recognize variability by noticing eg different sizes across a collection of items like buttons or blocks.

Interpret and communicate

  • Decide key results to summarize from an investigation and answer initial questions.
  • Communicate results eg with a data report, a poster, a video.
  • Start to make predictions eg: “it will happen, it won’t happen, it might happen”.

Grade K

Additional Details

Teaching Advice

  • Statistical Investigative Questions should be developed and refined about a topic students are interested in or have brought up themselves eg. a student wonders if everyone eats pizza. on Friday nights. The teacher then supports the refinement of the question.
  • Teachers should be ready to help form questions that can be addressed with data and support the framing of questions that may be used for data collection.

Ethics/Privacy

  • Consider fairness and fair share.
  • What types of questions are ok to ask in a survey?

With Thanks

With thanks to the people who gave feedback on the big ideas:

Dr. Pip Arnold
Director at Karekare Education, New Zealand

Dr. Denise Spangler
Dean of Mary Frances Early College of Education
University of Georgia, USA

Woodside School Video and Lesson Plan

Our thanks to the teachers from Woodside school for their help with the ideas.

Dr. Jo Boaler

Stanford Professor

Dr. Rob Gould

UCLA Professor

Cathy Williams

Cofounder YouCubed@Stanford

Dr. Steve Levitt

University of Chicago Professor

Tanya LaMar

Doctoral Student

Jack Dieckmann

YouCubed Research Director

Jesse Ramirez

Doctoral Student

Megan Selbach-Allen

Doctoral Student

References

Arnold, P., & Franklin, C. (2021). What Makes a Good Statistical Question?. Journal of Statistics and Data Science Education, 29(1), 122-130.

Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., & Spangler, D., (2020). Pre-K – 12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): A Framework for Statistics and Data Science Education. Retrieved from: https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf

K-12 Computer Science Framework Steering Committee. (2016). K-12 Computer Science Framework. ACM. Retrieved from: https://k12cs.org/

Seehorn, D., & Clayborn, L. (2017, March). CSTA K-12 CS Standards for All. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (pp. 730-730).