A practical course gives learners something concrete to reason about. A design has to fit its constraints. A build has to work. A test can reveal why it doesn't.
Research in Agriculture
At Royal Valley High School, I designed and taught a hands-on Research in Agriculture course. Its scope included electrical work, woodworking, basic engineering, 3D design and printing, small and gas engine repair, metalworking and CNC machining.
The course connected theory to working artifacts. My role included teaching practical skills and mentoring student projects as they moved from an idea toward something they could build and test.
The questions behind a build
A useful way to explain this teaching approach is through four questions:
- Design: What should the artifact do, and what constraints does it have?
- Build: What tools, materials and sequence will make the design workable?
- Test: What observation would tell us whether it works?
- Explain: What happened, what changed, and what would we try next?
This is an account of my teaching approach, not a reproduced assignment or a claim about a particular student's result.
Teaching across subjects
I also taught Physics, Chemistry, Plant and Soil Science, and Agriscience. These subjects offer different opportunities to connect an explanation to an experiment or a practical observation.
In higher education, my teaching included Python, R, biology and agroecology. Programming adds another kind of inspectable artifact: a program that can be run, tested and revised.
What carries into engineering
Teaching asks whether an explanation is usable by someone else. In engineering, that question applies to an interface, a runbook, a design discussion and a code review. Technical work becomes more useful when the next person can understand the assumptions and act on them.
Student work, names and photographs are not included here. The emphasis is on course scope and teaching practice.
Designing a course across practical disciplines
A course spanning electrical work, woodworking, 3D printing and engines cannot rely on a single kind of explanation. A drawing helps with geometry; a sequence helps with assembly; a measurement helps diagnose behavior. The teaching task is to choose the representation that lets a learner take the next useful step.
I also redesigned General Biology at Heritage University under COVID-19 protocols, aligning the laboratory manual with lectures and building exercises compatible with remote and hybrid delivery. That required preserving the connection between an explanation and a practical activity when the delivery conditions changed.
For a concrete example of how I explain data and uncertainty, inspect this newly authored R teaching note.