Simulation-Based Learning in Higher Education

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.

Simulation-Based Learning as an Alternative to Real-Life Situations

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.

Implementation of simulations

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.

Students gain complex skills through simulation-based learning

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.


Q: How does student voice and choice play a role in the design and implementation of simulation-based learning experiences?

A: Student voice and choice are crucial in shaping the design and implementation of simulation-based learning experiences. When educators actively seek and incorporate students' opinions, preferences, and interests into the development of simulations, the learning experiences become more relevant and engaging for students. This approach ensures that simulations are not only tailored to meet educational objectives but also resonate with students' real-world concerns and aspirations. Including student voice in the decision-making process can lead to more authentic scenarios, thereby enhancing the simulation's effectiveness in teaching complex skills. Furthermore, when students are involved in the creation or adaptation of their learning experiences, they are likely to feel a greater sense of ownership and engagement, which can positively impact their motivation and learning outcomes.

Q: In what ways are the effectiveness and impact of simulation-based learning assessed through text analysis or other student-generated data?

A: The effectiveness and impact of simulation-based learning can be assessed using text analysis of student-generated data, such as reflections, discussion posts, or written assignments. Text analysis allows educators to delve deeply into students' understanding, critical thinking, and problem-solving skills by examining the language and concepts students use to articulate their learning experiences. For instance, by analysing the complexity of language, the use of specific terminology related to the simulation topic, and the depth of reflection on problem-solving processes, educators can gauge the extent to which students have developed the intended complex skills. This method provides qualitative insights into student learning that might not be captured through traditional assessments, offering a richer, more nuanced understanding of the impact of simulation-based learning.

Q: How do simulations support diverse student needs and ensure equitable access to learning opportunities?

A: Simulations support diverse student needs by offering a variety of learning experiences that cater to different preferences, such as visual, auditory, kinaesthetic, and social learning. By incorporating a mix of real and virtual scenarios, role-plays, and team-based activities, simulations provide multiple pathways for students to engage with the content and develop complex skills. This flexibility ensures that students can find aspects of the simulation that align with their preferred learning methods, making the learning process more accessible and effective for everyone. To ensure equitable access to learning opportunities, educators can design simulations that are inclusive, taking into account potential barriers such as physical accessibility, technological requirements, and language diversity. By considering these factors, simulations can be made accessible to all students, regardless of their backgrounds or needs, promoting fairness and inclusivity in the learning environment.


[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.
DOI: 10.3102%2F0034654320933544

[1] Van Lehn, K . (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539
DOI: 10.1146/annurev.psych.47.1.513

[2] 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.
DOI: 10.1177%2F016146810911100905

[3] 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.
DOI: 10.1002/tea.10058

[4] Raven, J. (2000). Psychometrics, cognitive ability, and occupational performance. Review of Psychology, 7(1–2), 51–74.

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