TY - GEN
T1 - The Effect of Task Fidelity on Learning Curves
AU - Ritter, Frank E.
AU - McDermott, Ashley F.
N1 - Publisher Copyright:
© 2020 Proceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - What is the effect of level of simulation fidelity on learning and then on performance in the target task? We consider an example of an electronic maintenance training system with two levels of fidelity: a high fidelity (HiFi) simulation that basically takes as much time as the real-world task and a low fidelity (LoFi) simulation with minimal delays and many actions removed or reduced in fidelity and time. The LoFi simulation initially takes about one quarter of the time, and thus starts out providing about four times as many practice trials in a given time period. The time to perform the task modifies the learning curves for each system. The LoFi curve has a lower intercept and a steeper slope. For a small number of practice trials, this makes a significant difference. For longer time periods, the differences between low and high fidelity get smaller. Learners that move from low to high appear to not be adversely affected. We note factors that could influence this transfer (i.e., subtasks included in each simulation), and how this approach could be extended.
AB - What is the effect of level of simulation fidelity on learning and then on performance in the target task? We consider an example of an electronic maintenance training system with two levels of fidelity: a high fidelity (HiFi) simulation that basically takes as much time as the real-world task and a low fidelity (LoFi) simulation with minimal delays and many actions removed or reduced in fidelity and time. The LoFi simulation initially takes about one quarter of the time, and thus starts out providing about four times as many practice trials in a given time period. The time to perform the task modifies the learning curves for each system. The LoFi curve has a lower intercept and a steeper slope. For a small number of practice trials, this makes a significant difference. For longer time periods, the differences between low and high fidelity get smaller. Learners that move from low to high appear to not be adversely affected. We note factors that could influence this transfer (i.e., subtasks included in each simulation), and how this approach could be extended.
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M3 - Conference contribution
AN - SCOPUS:85161820983
T3 - Proceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling
BT - Proceedings of ICCM 2020 - 18th International Conference on Cognitive Modelling
A2 - Stewart, Terrence C.
PB - Applied Cognitive Science Lab, Penn State
T2 - 18th International Conference on Cognitive Modelling, ICCM 2020 - Co-located with the 53rd Annual Meeting of the Society for Mathematical Psychology
Y2 - 20 July 2020 through 31 July 2020
ER -