Some Evidence On The Nature Of Relearning Curves
Abbreviated Journal Title
RELEARNING CURVE; LEARNING CURVE; FORGETTING; ESTIMATING LABOR TIME; LEARNING-CURVE; Business, Finance
SYNOPSIS AND INTRODUCTION: A number of formal models have been used to reflect the fact that people generally require less time to perform a complex task after they acquire some familiarity and experience with the task. These models are referred to as learning curves, and a sizable literature exists on their properties and uses (Yelle 1979). In contrast, little has been written on the nature of relearning curves, which are formal models used to estimate the reduction in task time that occurs while relearning skills that have been forgotton due to interruption. The relatively few studies addressing this aspect of interrupted production advocate "backing up" the original learning curve (typically a log-linear model) in some fashion and using it to model the relearning process (Adler and Nanda 1974; Carlson and Rowe 1976; Cherrington et al. 1987; Cochran 1968; Hoffmann 1968; Keachie and Fontana 1966; Lippert 1976). This article reports on a laboratory study designed to provide some evidence on the nature of relearning curves. Subjects were paid a realistic wage to assemble structures with Erector Set parts. They repeated the task for approximately four hours, and three forms of marginal-time learning curves were fit to their performance data. After breaks ranging from seven to 175 days, they repeated the construction projects. The two sets of times were used to compute a measure of skill decrement and to fit nine forms of learning curves. Issues of interest are: (1) similarity of the functional forms of the best-fitting relearning and learning curves, (2) whether backing up the learning curve is a reasonable method of modeling relearning, and (3) effects of skill decrements on the relative goodness-of-fit of relearning curves. The results show that the best-fitting relearning curves are not of the log-linear form commonly used to model learning, but are of a form that is nonlinear on both a log-log and an arithmetic scale. Further, this new form developed in our study also provided a better fit than the log-linear model when applied to learning (pre-interruption) data. Additionally, backing up the best-fitting learning curve or starting anew with this same curve form also provided a good-fitting relearning curve model; however, backing up the classical log-linear model did not fit the data as well as other models. Finally, differences between the goodness-of-fit of the various models became more pronounced with the increase in amount of skill decrement. These findings should be of interest to those who use learning curves to estimate labor time, especially under conditions of interrupted production.
"Some Evidence On The Nature Of Relearning Curves" (1992). Faculty Bibliography 1990s. 400.