The Effect of Task Fidelity on Learning Curves: A Synthetic Analysis

Abstract

There have been discussions about the value of fidelity since simulation-based training systems have been created. A primary question, which has yet to be fully answered, is what is the effect of level of simulation fidelity on learning on a target task? We present a new analysis method and use it for several analyses of a training simulation for an electronic maintenance task with two levels of fidelity: a high-fidelity simulation that basically takes as much time as the real-world task and a low-fidelity simulation with minimal delays and many actions removed or reduced in fidelity and time. The analyses are based on the Keystroke-Level Model (KLM) and the power law of learning. The analyses predict that the performance on the low-fidelity simulation initially takes between one quarter and one eighth of the time of the high, and thus starts out providing between four and eight times as many practice trials in a given time period. The low-fidelity curve has a lower intercept and a steeper slope. Learners that move from low to high appear to not be adversely affected. For a small number of practice trials, this makes a significant difference. We also explore the effect of missing subtasks in the low-fidelity simulation. This effect varies with the tasks included: If the low-fidelity simulation does not train an important task, learners can be slower when they transfer. We also analyze a simulation that we have built and are studying. These analyses demonstrate that using lower fidelity training situations helps most where there is less time to practice, and that if there is extensive time to practice full fidelity has nearly the same outcome (but perhaps not the same costs or risks). We discuss how this analysis approach can help choose the level of fidelity of future training simulations.