[0302 Morning · Team 1] New maths materials for teaching children data science

Prompt: New maths materials for teaching children data science

Share your notes/comments here.

Research Questions

  1. Can middle-school students apply data-science methods and principles to a set of curated real-world problems?
  2. Are students in such a course able to generate meaningful datasets?

Highlights from the in-person discussion:

  • Yesterday there was a discussion about whether we really want students generating data sets: it’s great to turn this skepticism into a RQ!

  • How do you operationalise the word “meaningful”? The word may be too broad.

  • The first question is two questions in one (principles and methods are different things; you perhaps grasp principles and apply methods — perhaps split into two?)

Context

Data-based inferences are prevalent in everyone’s lives, and thus students should be able to infer any such data in an appropriate manner.

Purpose

Teach students to analyse and interpret data and identify fallacies in claims.

Inputs

  • 6th to 8th grade students.
  • Basic data sets such as charts in newspapers, cricket stats, etc.
  • Teachers with knowledge equivalent to 10th grade.

Constraints/Barriers

  • Possible lack of basic mathematical knowledge with students.
  • Lack of data interesting enough to engage middle-grade students.
  • Limited availability of passionate and trained teachers.

Activities

  • Ask students to create set of real-world data examples relatable to general student life such as household management, weather prediction, sports analysis, board games, etc and analyse the same.
  • Present data visualisations and explain features of the same.
  • Have students in pairs of groups present good and bad inferences using the same data sets of two sport players.

Outputs

  • Data sets generated by students from real-world examples.
  • Exercises related to these datasets.

Effects/Outcomes

  • Students should be able to understand, inspect and critique medium-sized data.
  • Students should be able to identify fallacies in data visualisations and claims.