An individual-based description of learning within an organization

The authors examine the problem of selecting a model for an individual-based representation of learning within a population of learners. Individual-based representations can be used to create distributions of learning patterns in the workplace. This is an alternate theoretical view of learning in organizations whereby performance is a unique attribute of each individual within the organization. Several published learning curve models are fitted to 3874 episodes of individual performance improvement. They conclude that a three-parameter hyperbolic function outperforms the other models for this application. This approach provides managers with: (1) distributions of between-worker variability with respect to rate of learning, prior learning, and steady-state production rates; (2) a quantitative measure of workforce learning that can provide information useful for workforce task-assignments; and (3) a methodological framework for selecting a most preferred individual model such that workforce distributions may be constructed and provide such information. Results indicate that workers perform in a region between the two extremes of fast improvement to a low level of productivity and slow improvement to a high level of productivity. Also, workers with more prior experience tend to have a higher steady-state productivity level.