▸“Wouldn’t it be cool if I could build that?”
▸“I wish I actually understood how that works.”
▸“I want to be able to explain this.”
▸“I’ve always wanted to learn how to do that.”
Load-Bearing Learning starts there.
Instead of forcing everyone through the same course, Load-Bearing Learning works backward from what you want to accomplish. It identifies the concepts that carry the load, orders their dependencies, and teaches them in the context of your final goal.
“I wish I could build / understand / accomplish this.”
Choose a capstone.
Find the concepts that actually carry the load.
Examples, diagrams, labs, and explanations reference your goal.
Build it. Explain it. Accomplish it.
Course 1 ↓ Course 2 ↓ Course 3 ↓ Course 4 ↓ “Hopefully I can use this someday.”
“I want to build this.”
↓
What does it require?
↓
What do those concepts require?
↓
Build the shortest useful path.
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Learn everything in context.Your goal isn’t the reward at the end of the curriculum. It’s what creates the curriculum.
In a building, a load-bearing structure supports everything above it. Knowledge works the same way.
Some concepts support dozens of things you may eventually want to understand or build. Others are useful only in specific situations.
Load-Bearing Learning maps those dependencies and helps you learn the concepts that actually support your goal — without requiring you to complete an entire traditional curriculum first.
◆ Self-Driving RC Car
▲
┌─────────┴─────────┐
│ │
Perception Motion Control
▲ ▲
Computer Vision Feedback
▲ ▲
Linear Algebra Calculus
└─────────┬─────────┘
▲
AlgebraIf your goal is to build a self-driving RC car, algebra shouldn’t feel like an unrelated math exercise.
Functions can describe steering response. Vectors can represent direction. Derivatives can describe changing velocity. Networking can explain how commands reach the car. Load-Bearing Learning can personalize examples, diagrams, exercises, and explanations around the capstone that motivated you in the first place.
y = 2x + 3steering angle = f(controller input)predicted value = f(input feature)ingredient quantity = f(number of guests)The underlying lesson stays canonical. The examples and diagrams adapt to the learner’s final goal.
Every concept lives inside a dependency graph. Capstones traverse that graph to find the knowledge required to accomplish a goal. Some goals stay within one skill tree. Others cross several.
○ algebra ─▶ ○ linear_algebra ─▶ ◆ computer_vision │ │ │ ▼ ▼ ▼ ○ functions ○ calculus ──────▶ ◆ sensor_fusion │ │ ▼ ▼ ○ networking ───────────────────▶ ◆ SELF-DRIVING RC CAR
LOAD-BEARING LEARNING
│
├── Mathematics
├── Computing & AI
├── Robotics & Physical Computing
└── Culinary Arts
├── Kitchen
└── Bar & BeverageMathematics
+
Computing & AI
+
Robotics & Physical Computing
↓
◆ SELF-DRIVING RC CARKitchen
+
Bar & Beverage
↓
◆ DINNER PARTY FOR 12+A capstone is a destination, not a course. Pick one and the graph computes the path that carries it.
Is this observed change meaningful, or is it probably noise?
How well is the model performing, and can we trust that result?
Did changing the preparation method actually improve guest ratings?
The concept remains canonical. The examples change to reinforce the learner’s goal.
├── Concept Graph ├── Learning Paths └── Capstones
├── Skills Survey 2.0 ├── Knowledge Assessments ├── Labs ├── Learning Games ├── Progress Tracking └── Submit Your Capstone
Have a “wouldn’t it be cool if…” of your own? Submit it and help expand the graph.
Tell us what you already know so your path can skip concepts you have already mastered.
Mathematics, Robotics & Physical Computing, Culinary Arts, and continued expansion of Computing & AI.
Practice concepts through interactive challenges.
Move from understanding a concept to actually using it.
Demonstrate mastery and identify concepts that need reinforcement.
Save learning paths, completed concepts, assessments, labs, and capstone progress.
People rarely become interested in algebra because they woke up wanting to study algebra.
That goal creates curiosity. Curiosity creates momentum.
Load-Bearing Learning tries to protect that momentum by teaching foundational knowledge in the context of what made you curious in the first place.
Our mission is to capture that spark of curiosity, keep the motivation behind it alive, and build a personalized path from “wouldn’t it be cool if…” to “I can do this.”
Have an idea, see something we’re missing, or want to help?
Until Submit Your Capstone ships, capstone suggestions come through here.