Models of Fatigue and Rest in Learning. Part I: Extension of the General Iterative Learning Model

Abstract. In this paper, classical iterative learning models are extended by including the factors of fatigue and rest. The existing approaches to model fatigue and rest from various fields––education, production, sports, and medicine––are analyzed, and the need to include these factors in the models is justified accordingly. Mathematical models are proposed to describe learning level dynamics during rest periods, considering the reduced efficiency of acquiring skills due to fatigue. Ten models of growing complexity are studied: from simple models without any fatigue effects to complex ones with the probabilities of skill acquisition and forgetting depending on time and rest periods. As is shown, the breaks of optimal duration allow improving the terminal learning level. In the model with fatigue, rest, and no forgetting, the optimal time to start rest is independent of the probability of skill acquisition as a function of time and lies at the middle of the experience acquisition interval. The models proposed are intended for predicting performance and optimizing training programs, production processes, and training cycles. This study emphasizes the need to consider biological and cognitive constraints when designing adaptive learning systems.

Keywords: experience, iterative learning, learning curve, mathematical modeling, fatigue, rest, skill acquisition, forgetting.