## 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.

Year level(s) Year 10
Audience Teacher
Purpose Teaching resource
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 content descriptions
ACMSP246 Describe the results of two- and three-step chance experiments, both with and without replacements, assign probabilities to outcomes and determine probabilities of events. Investigate the concept of independence
ACMSP247 Use the language of ‘if ....then, ‘given’, ‘of’, ‘knowing that’ to investigate conditional statements and identify common mistakes in interpreting such language
ACMSP278 Calculate and interpret the mean and standard deviation of data and use these to compare data sets
ACMSP251 Use scatter plots to investigate and comment on relationships between two numerical variables
ACMSP252 Investigate and describe bivariate numerical data where the independent variable is time
National numeracy learning progression Understanding chance - UnC3, UnC5
Interpreting and representing data - IRD6