By Marisa Graser
Applying knowledge in real-life situations has been proven to be a highly effective way for learners to acquire high level problem-solving skills (Van Lehn, 1996). However, including practical training into university teaching is challenging. Risks and ethical issues might arise if students are insufficiently prepared. Students may also feel overwhelmed without adequate guidance throughout. Additionally, specific practice scenarios like critical situations are less common in real-life settings and therefore not as easily trained. Overall, real-life practice can be inaccessible or less than ideal to achieve intended learning outcomes.
Chernikova et al. (2020) suggest the use of simulations to overcome these problems. Authentic problems can be created in a safe environment whilst reducing the complexity of the tasks on hand (Grossman et al. 2009). Simulations have therefore been commonly implemented into medical training, teacher education, engineering, and management.
In their meta-analysis, Chernikova et al. (2020) assessed several studies to identify criteria that help to successfully integrate simulations into higher education.
Firstly, they suggest that training a variety of skills helps to achieve an optimal outcome. Ideally, simulations would for example combine motor and sensory skills together with reasoning.
Secondly, combining different simulation types can have greater positive learning effects. This means that a mix of real simulations, like role plays or simulated discussions, and virtual simulations, like computer-based scenarios, are most effective.
With regards to the technology used, both real as well as computer-based simulations achieve the desired benefits. However, Chernikova et al. (2020) found some indication that virtual reality might enhance the positive effect of simulations even further.
Additionally, simulations with a high authenticity showed to have the largest benefit. Nevertheless, lower authenticity simulations are still highly effective and often less expensive and time consuming in their preparation.
Finding the right time point when to integrate a simulation into the course is also often of concern. Commonly, they are used towards the end of the programme, which ensures that students have sufficient prior knowledge and avoids overwhelming them. However, Chernikova et al. (2020) found that using simulations early on also has its benefits as it supports students in restructuring knowledge into higher order concepts. Here, it is important to provide more guidance to prevent cognitive overload.
In general, guidance and support can be provided at varying levels and in different ways. For students with low prior knowledge, Chernikova et al. (2020) suggest giving prompts, i.e. in the form of checklists, a set of rules, etc. Students with high prior knowledge on the other hand benefit from integrated reflection phases, i.e. on their own progress or problem solving skills. Other options include providing material in advance to give students prior knowledge, step-by-step guidance throughout, or assigning specific roles with defined actions or goals.
Overall, simulations are a great way to teach students complex skills. Critical thinking and problem solving are thereby the two key aspects. By giving students ill-structured problems (Shin et al., 2003), for example with multiple solutions or unclear rules and principles to solve them, students are forced to manage critical situations rather than just diagnosing and performing.
Additionally, communication skills are trained as communication is key for solving problems, i.e. when students need to get information from others (Raven, 2000), as well as for implementing the solution.
Lastly, students gain collaboration and teamwork skills when they work in groups or with multiple professionals, i.e. an emergency team.
Overall, simulation-based learning has been shown to have large positive effects on complex skill development across a broad range of disciplines. When compared to real practice, it might be even more effective, as the teacher can adjust the simulation as needed.
[Source] Chernikova O, Heitzmann N, Stadler M, Holzberger D, Seidel T, Fischer F. (2020) Simulation-Based Learning in Higher Education: A Meta-Analysis. Review of Educational Research, 90(4), 499-541.
 Van Lehn, K . (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539
 Grossman, P., Compton, C., Igra, D., Ronfeldt, M., Shahan, E., Williamson, P. (2009). Teaching practice: A cross-professional perspective. Teachers College Record, 111(9), 2055–2100.
 Shin, N., Jonassen, D. H., McGee, S. (2003). Predictors of well-structured and ill-structured problem solving in an astronomy simulation. Journal of Research in Science Teaching, 40(1), 6–33.
 Raven, J. (2000). Psychometrics, cognitive ability, and occupational performance. Review of Psychology, 7(1–2), 51–74.