RESUMEN
Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and group basis with the resulting estimates being useful to both learners and educators for feedback and educational quality improvement. In this primer, we review various descriptive and modelling techniques appropriate to learning curves including smoothing, regression modelling and application of the Thurstone model. Using an example dataset we demonstrate each technique as it specifically applies to learning curves and point out limitations.
Asunto(s)
Empleos en Salud/educación , Curva de Aprendizaje , Modelos Estadísticos , Evaluación Educacional/métodos , Humanos , Modelos EducacionalesRESUMEN
The learning curve during repetitive production and the associated forgetting during production breaks are fundamental issues in the understanding of behavior. A model is suggested that combines 3 basic findings, namely, that single memory traces decay according to a power function of the retention interval, that aggregated memory traces can be combined by integration, and that the time to produce a unit can be described by a diffusion process on the memory trace. This power integration diffusion model is validated with empirical data, and the result fits better than 14 other published forgetting models.