Mastery Learning (Bloom)

Bloom's model proposes that given sufficient time and instruction, virtually all students can master any objective. Traditional classrooms hold time constant and let achievement vary. Mastery learning reverses this: hold achievement constant (everyone reaches mastery), let time vary. The process: (1) define mastery criteria (80-90% on formative assessments), (2) teach and assess, (3) provide corrective instruction for non-masters while others do enrichment, (4) reassess until mastery before advancing. Bloom's '2 sigma problem' showed mastery learning with tutoring produced two standard deviations of improvement—the average tutored student outperformed 98% of controls.

When to use it

When designing sequential programs where later skills depend on earlier ones, when training consistently produces some competent and some incompetent graduates, when ensuring all learners reach a standard, or when diagnosing why employees fail at advanced tasks.

How it can help

For training designers: build mastery checkpoints into every program. Don't advance participants who haven't demonstrated prerequisite mastery—this causes cumulative failure. For managers onboarding new hires: ensure mastery of each skill level before advancing. For self-learners: don't move to the next chapter until you can demonstrate current mastery. The 2 sigma finding suggests most education failure is instructional design failure, not learner ability.