Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Calculating the relative effectiveness of expert coaching, peer learning, and cascade training

Reda SadkiGlobal health, Theory

A formula for calculating learning efficacy, (E), considering the importance of each criterion and the specific ratings for peer learning, is: This abstract formula provides a way to quantify learning efficacy, considering various educational criteria and their relative importance (weights) for effective learning. Variable  Definition Description  S Scalability Ability to accommodate a large number of learners  I Information fidelity Quality and reliability of information  C Cost effectiveness Financial efficiency of the learning method  F Feedback quality Quality of feedback received  U Uniformity Consistency of learning experience  Summary of five variables that contribute to learning efficacy Weights for each variables are derived from empirical data and expert consensus. All values are on a scale of 0-4, with a “4” representing the highest level. Scalability Information fidelity Cost-benefit Feedback quality Uniformity 4.00 3.00 4.00 3.00 1.00 Assigned weights Here is a summary table including all values for each criterion, learning efficacy calculated …

Why does cascade training fail

Why does cascade training fail?

Reda SadkiGlobal health, Theory

Cascade training remains widely used in global health. Cascade training can look great on paper: an expert trains a small group who, in turn, train others, thereby theoretically scaling the knowledge across an organization. It attempts to combine the advantages of expert coaching and peer learning by passing knowledge down a hierarchy. However, despite its promise and persistent use, cascade training is plagued by several factors that often lead to its failure. This is well-documented in the field of learning, but largely unknown (or ignored) in global health. What are the mechanics of this known inefficacy? Here are four factors that contribute to the failure of cascade training 1. Information loss Consider a model where an expert holds a knowledge set K. In each subsequent layer of the cascade, α percentage of the knowledge is lost: 2. Lack of feedback In a cascade model, only the first layer receives feedback …