Description:
This is Part 1 of a 2-part series.
Computational and algorithmic thinking are increasingly recognised as essential components of contemporary mathematics education. While formally embedded within the Digital Technologies curriculum, their explicit integration into mathematics classrooms remains limited, with programming often taught in isolation from mathematical reasoning. This webinar addresses that gap by introducing Python as a practical tool for expressing and testing mathematical procedures, rather than as a separate programming subject.
This session is designed for mathematics teachers new to Python. It focuses on the transition from pseudocode to executable code. Participants will explore how familiar step-by-step mathematical methods can be expressed precisely using pseudocode: making assumptions, decisions, and stopping conditions explicit, before translating them into Python. A carefully selected set of Python foundations (data types, conditions, control flow, functions, and simple libraries) will be introduced as they naturally arise from pseudocode structures.
Through examples drawn from iterative processes and numerical methods, participants will see how Python can simulate experiments, generate data, and visualise outcomes. The emphasis is on reading, running, and adapting code rather than writing programs from scratch, so no prior coding experience is required.
By the end of the session, teachers will have a clearer framework for linking pseudocode, algorithmic thinking, and Python; greater confidence in engaging with simple code, and practical ideas for using computation to support mathematical explanation, experimentation, and discussion in the classroom.
Materials:
Reference guide for essential Python syntax
Side-by-side comparison sheet showing pseudocode and Python translations
Worked examples available in GitHub repository
Dr Robin Wang
Dr Robin Wang is a specialist mathematics and algorithmics educator with a PhD in Computer Systems Engineering and over ten years’ experience teaching in Australian secondary schools. He is a regular presenter at mathematics and computing education conferences, sharing classroom-tested approaches for integrating computational and algorithmic thinking into mathematics education.
Robin currently teaches at the Centre for Higher Education Studies (CHES) in Melbourne, where he works at the interface of secondary and tertiary study, supporting high-achieving students in developing mathematical and computational thinking skills. His pedagogical approach centres on using Python as a tool for mathematical reasoning, algorithmic thinking, and real-world problem solving.
He is the author of A Not Too Short Introduction to Python, When Maths Meets Python: 100 Coding Experiments for Problem Solvers, and From Numpy to NetworkX: A Pythonic Adventure in Mathematics. Through these works, Robin demonstrates how computational thinking can enrich mathematics learning by abstraction, modelling, and generalisation.
Registrations close 20th July 2026
| 
