The Application of an Absorbing Markov Chain in Prediction Learning

A method is presented that utilizes the theory of Markov chains in predicting learning for manual activities. The model is applied to a realistic example and the results compared with learning curve theory. The results illustrate that a Markov chain approach to learning can give a good approximation to a real life situation. Recommendations are made for further applications of this model to actual situations, such as production lines. Areas of additional research are also discussed.