Response time data in learning experiments show a typical trend. They start out slow, quickly improve, before finally tending toward optimal performance. This trend provides critical information that can be used to test various theories of learning. One convenient way to characterize the data is the use of a learning curve; an idealized curve that passes through the observed data points as a function of training. This idealized curve has free parameters that must be estimated using optimization techniques. In this tutorial, we show how to estimate learning curve parameters using three softwares (Excel, SPSS, and Mathematica) assuming that the idealized curve is a power function. The techniques can easily be adapted to other functions. Finally, details are provided on related topics (maximizing block sizes, testing curvatures, etc.).

UR - http://www.tqmp.org/RegularArticles/vol02-2/p077/p077.pdf RP - IN FILE DO - 10.20982/tqmp.02.2.p077 DA - 2006-09-01 ER -