Teaching context and philosophy

Teaching Context

My teaching experience spans five institutions and diverse student populations—from gifted high-school students through the Canberra Computer Science Enrichment program to mid-career military officers at the Australian War College. I have developed and delivered 20+ courses across undergraduate and graduate levels in computer science, mathematics, and engineering, with cohort sizes ranging from 10 to 200+ students. At UNSW Canberra, I currently teach Surveillance Technologies and Communication and Information Systems to military officers and served as course convenor for Computational Problem Solving, a large first-year engineering course where I led a teaching team of eight members.

Teaching Philosophy

My teaching philosophy centers on making complex technical concepts accessible through innovative pedagogical approaches that adapt to evolving educational landscapes. I believe in proactive innovation, adopting teaching methods not as reactions to challenges, but as fundamental improvements to student learning.

Flipped Learning and Active Engagement: Years before generative AI became a concern, I adopted flipped learning methodologies for programming courses. Students watch pre-recorded lectures before class, post questions online, and engage in active problem-solving during class time. This approach naturally addresses AI-related challenges by shifting focus from content delivery to critical thinking and application.

Collaborative Learning with Technology: In Surveillance Technologies, students use LLMs to implement simulations based on statements of work, then share their approaches on discussion forums. Peers attempt the same problems with different LLMs and suggest improvements. This fosters engagement, develops critical evaluation skills, and teaches students to use AI as a collaborative tool for solving real engineering problems, even without extensive programming backgrounds.

Tangible, Experiential Learning: I developed an educational robotics platform where students learn programming by controlling physical robots in an arena. This hands-on approach makes abstract concepts tangible, significantly improving knowledge retention and student engagement. I'm currently developing a second-generation platform for deployment at SET.

Visual and Accessible Explanations: Having taught across mathematical, engineering, and computer science domains, I've learned that even complex mathematical concepts benefit from visualization. My lecture materials emphasize visual representations and real-world examples that build student intuition before diving into formal abstractions.

Student-Centered Adaptation: My teaching during COVID-19 exemplifies my adaptive approach. I developed automated assessment systems using GitHub Actions for immediate feedback and employed GradeScope for detailed rubric-based evaluation, maintaining educational quality while addressing unprecedented challenges.

In some courses this philosophy has resulted in strong student evaluations and student feedback highlighting my ability to make "dry computer concepts more fun and accessible for beginners," while others still need improvement. My approach focuses on continuous improvement and bridges theoretical rigor with practical application, preparing students not just to solve problems, but to think critically in an AI-augmented world.