I’ve taught science and programming across secondary and higher education, connecting explanations to experiments, programs and working artifacts.
Course story / Royal Valley High School
From an idea to something that works.
A hands-on Research in Agriculture course connected design, tools, construction and testing. Its scope ranged from 3D printing and woodworking to electrical work and engine repair.
I want learners to have something they can inspect, test and revise. In science that may be an experiment. In programming it may be a function. In a build course it may be an artifact.
The shared question: what evidence tells us that the idea works?
Teaching experience
2023–2025 / Secondary education
Royal Valley High School
Physics, Chemistry, Plant and Soil Science, Agriscience, and a hands-on Research in Agriculture course.
2021–2024 / Higher education
Heritage University
Adjunct teaching in biology and programming, including laboratory exercises and applied learning.
2022–2024 / Higher education
Zambian Institute of Agriculture
Visiting teaching in programming and agroecology, with curricula adapted for virtual delivery.
2017–2020 / Teaching support
Washburn University & Washington State University
Biology tutoring and teaching assistance at Washburn; insect taxonomy teaching assistance at WSU.
Course design / Making the explanation usable
Three different teaching problems.
Heritage University / Biology
Keep the lab connected to the lecture.
I redesigned General Biology under COVID-19 protocols, aligning the lab manual with lectures and building exercises for remote and hybrid delivery.
Introductory R / Applied statistics
Turn a question into an analysis.
A six-week applied statistical-analysis project connected programming with data, interpretation and explanation.
Royal Valley / Research in Agriculture
Make an idea work in materials.
I designed and taught a build course spanning electrical work, woodworking, 3D printing and engines, mentoring projects from theory toward working artifacts.
Two small synthetic datasets share a mean of 10. Their sample standard deviations are 1 and 9. Start with the observations, then ask what the summary hides.