One of the earliest renditions of simulation-based learning is credited to Edwin Link. He created the modern flight simulator predecessor between 1929-1931 in response to World War I, where more pilot and plane losses were attributed to accidents rather than combat (Hays & Singer, 1989). Since then, simulation usage and design have progressively improved, with simulation-based practices currently being utilized across several industries with a powerful presence in the military, aviation, and health care industry as a means to conduct training, evaluation, and research. Simulations are defined as “approximations to the reality that require trainees to react to problems or conditions as they would under genuine circumstances” (Tekian, McGuire, & McGaghie, n.d.). Examples of commonly used simulation devices include VR Head Mounted Displays (HMDs), computer-operated life-size medical mannequins, full-motion flight simulators, and various computer-based simulation programs (e.g., driving simulator, industrial processes simulator, machinery operator simulator, etc.). A few of the benefits offered by simulations over traditional learning methods include long-term cost reduction, the experience of alternative conditions and courses of action, provides a realistic job preview, a more effortless transfer of training to the operational environment, provides a practice setting without risk of harm nor negative consequences. It produces a lower carbon footprint (Myers, Starr, & Mullins, 2018).

The Fidelity Question

As technological advancements continue to enhance the capabilities of simulation design, the question of simulation fidelity (the degree to which a simulation device can replicate the actual environment (Gross et al. (1999); Alessi (1988)) and its relationship to learning effectiveness have become a highly debated topic among instructional design and training professionals. Framed as the fidelity question researchers are asking – how similar to the actual task situation must a training situation be to provide practical training? And further, does maximum fidelity equal maximum transfer of training? To answer these questions, we first need to understand the construct of fidelity better.

Simulator-based training is often categorized as either low-fidelity simulations (LFS) or high-fidelity simulations (HFS). In general, high-fidelity refers to simulations that more closely replicate the actual environment or feel more ‘real.’ In contrast, low-fidelity refers to simulations that are typically simpler in design and functionality and may only replicate certain aspects of the environment. It should also be noted that there are no standardized parameters to distinguish low from high fidelity simulation, nor a universal definition of fidelity, as the meaning and definition of each may vary depending on the industry and the designated environment being simulated. This lack of construct validity is one of the main challenges observed in infidelity research.

When considering the best method for measuring simulation fidelity, it would be inaccurate to look at simulation fidelity as a singular variable, as it is by definition the culmination of the experience that occurs when all seven fidelity types merge. As shown in Table 1 below, there are eight distinct definitions of fidelity, including simulation fidelity’s combined factor. These fidelity types serve a different function within the simulated environment and should be taken into consideration both as a whole and individually in the calculation of total simulation fidelity.

Table 1: Fidelity Definitions (Hancock et al., 2019)

Fidelity type References Definition
Simulation fidelity Gross et al. (1999); Alessi (1988) The degree to which the device can replicate the actual environment, or how “real” the simulation appears and feels.
Physical fidelity Allen (1986) The degree to which the device looks, sounds, and feels like the actual environment.
Visual-audio fidelity Rinalducci (1996) Replication of visual and auditory stimulus.
Equipment fidelity Zhang (1993) Replication of actual equipment hardware and software.
Motion fidelity Kaiser and Schroeder (2003) Replication of motion cues felt in the actual environment.
Psychological fidelity Kaiser and Schroeder (2003) The degree to which the device replicates psychological and cognitive factors (i.e., communication, situational awareness).
Task fidelity Zhang (1993); Roza (2000); Hughes and Rolek (2003) Replication of tasks and maneuvers executed by the user.
Functional fidelity Allen (1986) How the device functions, works, and provides actual stimuli as the actual environment.


Table 1 lists several definitions of fidelity as defined in research. Simulation fidelity provides the fundamental purpose of fidelity in simulation experience, describing how “real” the simulation appears and feels. Meanwhile, the other definitions of fidelity can, for the most part, be broken into two main categories: those that describe the physical experience and those that represent the psychological or cognitive experience (Hancock et al., 2019). The most commonly discussed fidelity category is physical fidelity which encompasses visual-audio fidelity, equipment fidelity, and motion fidelity. These combine to simulate the look, sound, feel, and occasionally smell of the environment. While the second category of psychological-cognitive fidelity goes beyond the look and feel of the simulation to describe the degree to which the user is psychologically and cognitively engaged in the same manner when compared to the degree to which the actual environment would engage the user (examples: simulated stress and workload). The remaining fidelity types of task fidelity and functional fidelity are concerned with how the user interacts with the simulated environment including the degree to which the simulator replicates the tasks of the environment and the degree to which the simulator reacts to performed tasks as they are executed by the user.

Fidelity and Transfer of Training

The main goal of a training simulator is to promote the development of a skill, ability, or area of knowledge that is required for the successful completion of a target task. The effectiveness of the simulator thus depends on the extent to which the acquired knowledge/skills/abilities through practicing the simulated task can be transferred to the target task.

Intuitively, a positive correlation between the degree of realism of a simulator and the effect on transfer of training would be assumed, especially as it is supported by a number of theories including the theory of identical elements (Thorndike, 1913) which states that the most effective transfer of skills occurs between simulator and the operational environment when both share common elements. But, despite this assumption and theory, numerous studies have found no distinct advantage of High Fidelity (HF) compared to Low Fidelity (LF) simulation with regards to improvement of knowledge or skills, with several studies even reporting increased declarative knowledge of participants in the LF simulation groups. These results reveal that there are likely several other important factors that need to be considered in the transfer of simulation-based training apart from simulation fidelity.

One of the models discussed fairly regularly in the articles which failed to prove the high-fidelity advantage was the “Alessi Hypothesis,” which provides several theorized explanations for the failed transfer of training. The first theorized explanation states that there is a certain point at which adding too much fidelity results in negative learning experiences as high fidelity equals high complexity, which requires more cognitive skills thus increasing trainee workload, which in turn impedes participant learning (Alessi, 1988). The second theorized explanation discusses the connection between fidelity and learning describing it as a nonlinear relationship largely dependent on other factors such as the trainees’ experience level and stage of instruction. Meaning, to experience optimized training effectiveness, the degree of fidelity in a simulation should attempt to match the level of difficulty expressed by the learning objective as well as the training stage of the learner (Alessi, 1988). Depicted in Figure 1 is Alessi’s model of the relationship between the degree of fidelity and learning for novice, experienced learners, and expert learners.

Figure 1: Degree of fidelity and stage of learner (Alessi, 1988)

This strategy of defining the level of capability and training objectives first followed by the degree of fidelity not only makes practical sense but would also contribute to cost reduction in the overall use of simulation-based education. In another article written on maritime training facilities, they describe their strategy of keeping training costs low while maximizing training effect by employing a similar strategy of utilizing LF simulators in the initial stages of learning to familiarize and train basic skills, while developing HF simulators to train advanced technical and non-technical skills (Renganayagalu, et. al., 2019).

Relevant Theories

Cognitive Load Theory (CLT)

In addition to the model above, a number of developed theories were also discussed in the research, both to explain possible reasons for lack of transfer in HF simulation, as well as to guide future simulation development. One of the most commonly referenced theories in the explanation of the failure to transfer includes the cognitive load theory (CLT), an instructional theory that describes learning and problem solving within the context of how information is processed and addresses the limitations of working memory. While long-term memory has a limitless capacity, working memory is limited to five to nine informational elements at any given time, with many of those elements forgotten within 20 seconds, unless rehearsed or practiced (van Merrienboer & Sweller, 2010). Therefore, if the cognitive load is too high, learning and performance will be affected as the learner is not able to properly process and retain the content being delivered. In the case of high-fidelity simulations which involves completing tasks with a high level of intractability and often through the manipulation of multiple elements at once, increased cognitive load is highly possible, especially in the case of novice learners. To combat this, one study, in particular, held an introductory course covering the fundamental basics of the training to decrease the initial level of cognitive load trainees would experience.

NLN Jeffries Simulation Theory

In addition to the cognitive load theory there was one other theory mentioned throughout the research, but in relation to future recommendations for simulation, development to ensure that transfer of training occurs. This theory known as the NLN Jeffries Simulation Theory (2005, 2007, 2012) is a theoretical framework that has received extensive empirical support and is recommended as a guide in the development of simulated experiences. The framework is composed of seven aspects, beginning with the background and design aspects which should be considered before the simulation experience and define simulation goals and resource allocation, the four aspects involved in the conducting of the simulation experience including the facilitator, the participant, the educational strategies, and the dynamic relationship between each of them, and finally, the aspect which defines the outcomes of the simulation including participant reaction, learning development, and behavioral transfer. As seen in the model below this process is intended to aid instructional designers in the implementation of an effective simulation design, from developing objectives, to evaluating effectiveness.

Figure 2: NLN Jeffries Simulation Theory

Additional Participant Outcomes Related to HFS

Even though the main body of this article is meant to examine the relationship between fidelity and learning effectiveness, through the review of the literature there were several other participant outcomes demonstrated to have a relationship with high-fidelity simulations. These outcomes include heightened levels of participant self-efficacy, stress, and self-confidence in skill deployment.

Increased Self-Efficacy

Increased self-efficacy was one of the most commonly discussed factors in relation to high-fidelity simulations. Perceived self-efficacy concerns an individual’s perception of self-confidence to successfully complete a task (Bandura, 1977) and is believed to be influential on the student’s level of performance, choice of tasks, and the amount of effort put into performing those tasks. Self-efficacy which has been acquired before or during training leads to an increased motivation to learn and better learning outcomes (Salas et al., 2012). One article for example utilized a measure of self-efficacy which was given to participants at the beginning, middle, and end of their designated simulation training, and found that those who participated in the high-fidelity simulation showed statistically significant improvement in self-efficacy following each completion of the survey, as compared to the control group who only showed improvement once during the survey completion. Additionally, another study that examined high-fidelity and self-efficacy in law enforcement officers found that high fidelity increased self-efficacy, emotional arousal, and led to positive training transfer from the lessons learned in the simulator scenarios.

Increased Stress

Another factor observed to be unique to high-fidelity simulations was the increased level of stress experienced by participants as compared to those in low-fidelity simulation. Some of the possible explanations given for this increased stress response included the amplified level of external audio and visual stimuli, as well as the hyper-realistic form factor of the simulated patient who in this specific simulation was bleeding and outwardly experiencing pain. Even though stress from an outside consideration may be considered a negative experience, the authors of this article argued that induced stress during high-fidelity simulation may be beneficial for the participants as they may be able to develop their stress management skills within this simulated environment to be carried over into actual clinical practice.

Increased Self-Confidence

One article, in particular, attempted to assess the level of confidence participants felt in employing skills learned during the simulation-based training, after having found inflated levels of self-confidence in participants of previously conducted studies using high-fidelity simulation. In this article, participants were randomly assigned to two groups of LF and HF simulation during curricular advanced life support (ALS) training courses. Before the course, 69% of participants in the HF group were assumed to have a significant advantage over the LF group in skill development, which did not significantly decrease over the training, with 53% of the participants still reporting assumed advantage at completion. Additionally, 41% of students in the HF group considered themselves better performers in handling resuscitation despite having no knowledge of the LD group’s training process. Even though confidence of skill development would typically be considered a positive outcome, in this case, the authors of this article wanted to specifically highlight the dangers associated with overly elevated levels of confidence after the completion of an HFS as there is a positive link between overconfidence and risk-taking behaviors.

Additional Distinctions Between Effective & Non-Effective HFS

Since this article has refuted the notion that high-fidelity alone can predict simulation effectiveness, it may be helpful for future applications to recognize the simulation features, when combined with high-fidelity simulation, to help to produce better results (Issenberg et. al., 2005).

  1. Providing feedback – 47% of journal articles report educational feedback is the most important feature of high-fidelity simulation-based education
  2. Repetitive practice – 39% of journal articles identified repetitive practice as a key feature involving the use of high-fidelity simulations in education
  3. Curriculum integration – 25% of journal articles cited integration of simulation-based exercises into the educational curriculum as an essential feature of their effective use
  4. Range of difficulty level – 14% of journal articles address the importance of the range of task difficulty level as an important variable in simulation-based education
  5. Multiple learning strategies – 10% of journal articles identified the adaptability of high-fidelity simulations to multiple learning strategies as an important factor in their educational effectiveness
  6. Capture clinical variation – 10% of journal articles cited simulators that capture a wide variety of clinical conditions as more useful than those with a narrow range
  7. Controlled environment – 9% of journal articles emphasized the importance of using high-fidelity simulations in a controlled environment where learners can make, detect, and correct errors without adverse consequences
  8. Individualized learning – 9% of journal articles highlighted the importance of having reproducible, standardized educational experiences where learners are active participants, not passive bystanders

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