How to Use the Perfect Reinforcement Flow

perfect reinforcement flow graphic showing the charting of challenge vs. skills

Most training experiences focus on teaching learners exhausting amounts of new information at once. How do you structure a training reinforcement program to help learners remember what they've learned without exhausting them?

3 Tips to Use the Perfect Reinforcement Flow

  1. If you don’t give your learners a chance to rest, they’ll take it anyway.
  2. Promote engagement by using structured training goals and reinforcement objectives.  
  3. Use frequent feedback to shape behavior change. 

Find a Balance Between Challenge and Satisfaction

Mihaly Csikszentmihalyi, Hungarian psychologist, introduced the theory, “Flow: The Psychology of Optimal Experience,” which focuses on finding a balance between challenge and ability.

When the challenge far exceeds a learner’s ability, they quickly become frustrated and abandon the task. On the other hand, when something is far too easy for a learner, they're bored and unlikely to continue reinforcement.

So how do you design a program that includes the perfect balance of challenge and satisfaction? 

Focus on the End Goal

To design an effective training reinforcement program, you must structure your program around measurable training goals and reinforcement objectives.

Challenge your learners with short-term goals that ultimately lead to them accomplishing the larger reinforcement objectives.

Create Feedback Loops

Learners must be able to tell how they're progressing through a training reinforcement program. An impactful reinforcement course incorporates manager support and frequent opportunities for the learner to provide feedback.

Use gamification to incorporate feedback and promote engagement with reinforcement progress, leaderboards, points, and visual cues.  

Learn more about creating the perfect reinforcement flow and other key principles in our eBook: From the Olympics to the 7 Principles of Reinforcement.


This post was originally published on June 13, 2017.

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