Learning science studies how people come to know and remember things. Instructional design turns that knowledge into lessons, courses and software. For an EdTech company, the two together answer the only question that justifies the product: does using it cause people to learn more, faster, or more durably than they would have otherwise? This section covers the findings that hold up best (retrieval practice, spacing, interleaving, worked examples, cognitive load, feedback and mastery learning), Bloom's "two-sigma" claim and why it is mostly misquoted, the design models practitioners use, and how to build these findings into a product without letting engagement metrics quietly replace learning.
What the study covers
- 1Overview: why learning science matters commercially
- 2Mechanics: the findings that hold up
- 3Instructional design: models and process
- 4Key numbers
- 5Bloom's two sigma, briefly
- 6Translating research into product
- 7Landmark companies and cases
- 8Pitfalls
- 9How an expert thinks about it
- 10Glossary
Technical exhibits
Worked examples, checklists and case studies included with this study.
Spaced Repetition Worked Example
Forgetting-curve arithmetic computed in Python: review intervals, reviews per item per year, daily review load, massed vs spaced retention
Two Sigma Case Study
What Bloom (1984) actually reported, why later estimates are far smaller, and an effect-size-to-percentile translation table
Research to Product Checklist
A design-review checklist for turning learning-science principles into product features, and for spotting where engagement mechanics fight learning