reSolve: Algorithmic thinking – Simulation

This unit shows why and how simulation complements more traditional mathematical methods of investigation and investigates the comparative advantages and disadvantages of simulation-based approaches. Students work hands-on with interactive simulations and learn the role of virtual experiments, gaining exposure to computational methods. They learn the basics of some fundamental simulation methods, including Monte-Carlo simulations, agent-based simulation, individual-based and population-based simulation.

Additional details

Year level(s) Year 10
Audience Teacher
Purpose Teaching resource
Format Downloadable resources
Teaching strategies and pedagogical approaches Mathematics investigation
Keywords data representation, data analysis, modelling, simulations, chance experiments, Monte-Carlo simulation

Curriculum alignment

Curriculum connections Technologies, Numeracy, STEM/STEAM
Strand and focus Statistics, Probability, Build understanding, Apply understanding
Topics Data representation and interpretation, Chance and probability
AC: Mathematics (V9.0) content descriptions

Design and conduct repeated chance experiments and simulations using digital tools to model conditional probability and interpret results


Use the language of “if .... then”, “given”, “of”, “knowing that” to describe and interpret situations involving conditional probability

Construct scatterplots and comment on the association between the 2 numerical variables in terms of strength, direction and linearity

Construct two-way tables and discuss possible relationship between categorical variables

Numeracy progression Understanding chance (P6)

Copyright details


ReSolve: Maths by Inquiry


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