The Learning of Robust and Flexible Skills with Respect to System Failures in a Flight Management System

In this thesis, I describe an experiment and introduce an ACT-R cognitive model used to investigate the learning of robust and flexible skills. In the study, 60 students participated in an experiment solving problems in a simulation of a Flight Management System. In order to investigate robustness and flexibility, state and display malfunctions would occur during the experiment. An instruction based on a list of steps, and an instruction based on pre- and postconditions of the environment were used to manipulate the use of internal and external control states. Using mixed effect models, the experimental data and model’s data are compared. The model captures the overall performance of the participants in terms of reaction times and correctness scores. The results reveal that visual input is important in skill performance, and that a focus on pre- and postconditions aid participants to cope with system malfunctions.