# Effectiveness of Driving Simulators for Drivers' Training: A Systematic Review
## 1 Introduction
### Theories of Performance and Implications for Simulation-Based Training (SBT) Measurement  
[PERSON] et al., 2009 [32], provided a review of the state of the science on simulation-based training (SBT) performance measurement systems. It states that training using traditional methods, such as lectures or conferences, is insufficient to meet the demands of many modern work environments or organizations. That is why they are resorting to the use of simulators to transfer this knowledge, which is usually very practical and oriented to situations that participants will encounter in the real world. The effectiveness of all the aforementioned types of simulator requires a set of generic/standardized actions, such as a guided training plan and a continuous measurement of staff performance, so that their aptitude can be evaluated and the training can be fed back by these measurements. If a performance measurement is not correctly adjusted for individual or teamwork use, it will certainly lead to a waste of time and money for both the trainee and the company providing the training. There is currently a wide variety of theories on how performance should be measured and its implications in SBT-based systems, for both individual and collective learning (teamwork). For the measurement of individual performance, the work of [PERSON] et al. [33] stands out; they state that performance depends on three variables: declarative knowledge, i.e., the facts and knowledge necessary to complete a task (understanding the task requirements); procedural knowledge and skills, i.e., the combination of knowing what to do and how to do it correctly; and, finally, motivation, i.e., the combination of the expenditure, level, and persistence of effort required for learning. Regarding the measurement of team performance, there is a wide discussion [34; 35] on the definitions of performance and its effectiveness. This has resulted in the formation of different frameworks depending on the specific learning context. Training a flight crew is not the same as training a marketing or human resources team. Among the different existing theoretical frameworks for measuring team performance in SBT are input-process-output (IPO) [36; 37; 38], shared mental models [39; 40; 41], adaptability [42; 43], the \"big five\" of teamwork [43; 44; 45; 46; 47], and macroognition/team cognition [48]. These theories require certain methods for their application in measuring SBT performance and feedback. [PERSON] classifies these methods according to whether they are qualitative or quantitative. Those of the former type are used to define the simulation system to be developed and the measurements to be made. These include protocol analysis [49], the critical incident technique [50], and conceptual maps [51]. The latter type consists of those which, based on the former, quantify the developed processes and provide feedback to the system to correct what is necessary. These include behaviorally anchored rating scales, or BARS [52]; behavioral observation scales, or BOS [53]; communication analyses [54]; event-based measurement, or EBAT [55]; structural behavioral assessment [39]; self-report measures; and, finally, automated performance recording and measurement [55]. Simulation is a field that is increasingly incorporated into our daily lives. In recent years, research has emerged with new methods to measure simulation-based learning. [PERSON] et al. [56] proposed a novel and well-established model to measure the user experience and usability of a simulation-based training application. This method combines the perceived usefulness, the perceived ease of use, the behavioral intention to use the system, and two more external variables to conclude the user acceptance of the simulation system. Some years before, the same authors proposed a similar method based on the evaluation of external variables considered to be \"strong predictors\" [57]. As can be seen, the variability of measurement types for performance in simulation-based coaches is enormous.