Learning rate and subjective mental workload in five truck driving tasks

Both learning curve models and subjective mental workload are useful tools for determining the length of training for new workers and predicting future task performance. An experiment was designed to collect the task completion times and subjective mental workload of five driving tasks including (a) reverse into garage, (b) 3-point turn, (c) parallel parking, (d) S-curve and (e) up-down-hill. The results indicated that task completion times of truck driving can be predicted with a learning curve. Practice significantly reduced the mental workload rating. However, the novice trainees tended to have a more significant reduction because, compared to experienced trainees, they tended to give greater or lower workload scores than the experienced trainees before and after practice, respectively. The current study may not be complete enough to provide guidelines for a training programme, but it is adequate to suggest that learning rate and workload measure can serve as indexes for factoring in the individual differences. Practitioner summary: Learning curves can be used to determine the length of training for new workers and performance standards for a particular task. Learning rate and mental workload were found to be important measures for comparing individual differences in order to better design a training programme. However, mental workload must be evaluated by experienced participants.