Four Strategies for Developing Student Simulations

07.17_four-strategies-for-developing-student-simulations

Higher education has traditionally taken students out of the “real world” by placing them in the artificial world of a classroom to learn. But the digital revolution made it possible to reintroduce real-world learning through student simulations. Instructors can now record videos of hypothetical situations and give students the opportunity to make decisions that affect how those situations play out as a means of preparing them to implement their learning after graduation.

Unfortunately, many simulations amount to nothing more than a series of multiple-choice questions connected by a contrived situation, which does not offer students any more learning than they would get from a simple worksheet. As Shellman and Turan (2006) note, a simulation should add value to learning by (a) fostering creative and analytical thinking and (b) teaching students about the situation being simulated. Here are four ways to promote these learning outcomes.


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Higher education has traditionally taken students out of the “real world” by placing them in the artificial world of a classroom to learn. But the digital revolution made it possible to reintroduce real-world learning through student simulations. Instructors can now record videos of hypothetical situations and give students the opportunity to make decisions that affect how those situations play out as a means of preparing them to implement their learning after graduation.

Unfortunately, many simulations amount to nothing more than a series of multiple-choice questions connected by a contrived situation, which does not offer students any more learning than they would get from a simple worksheet. As Shellman and Turan (2006) note, a simulation should add value to learning by (a) fostering creative and analytical thinking and (b) teaching students about the situation being simulated. Here are four ways to promote these learning outcomes.

Providing more and less information than needed

As Dan Meyer notes in his exceptional TED Talk, math courses give students artificial problems by providing them with exactly the information they need to solve the problem. They can just scan the textbook to find the right formula, plug in the information they are given, and produce the answer without even understanding what they did. By contrast, real-life situations do not come with the relevant information already tagged for us; they require us to determine what information we need to solve the problem and to find that information.

Instructors can implement this principle in simulations by providing students with both more and less information than they need to solve the problem. A simulation that puts students into the role of a civil engineer contracted to determine why a building is leaning and what can be done to fix it should provide information from the point of view of a client who does not know what information is relevant and thus provides both more and less needed information than the student needs. For instance, the simulation might include irrelevant information about non-load-bearing walls but also leave out important information that the student needs to get by asking the client.

Branching scenarios with good and bad outcomes

A fundamental characteristic of a simulation is that student inputs influence simulation outputs. This means that branching scenarios take students to both good and bad outcomes according to their choices. For instance, a key lesson in accounting classes is that there are various ways to set up a partnership contract, and an apparently equitable division of profits and losses can lead to some surprising results. A partnership can suffer a loss, but the contract could be crafted so that one partner actually makes money while the other both suffers the entire loss and pays the partner a profit. Ideally, an accounting simulation on making a partnership contract would require students to make decisions about a range of typical contract variables and then use the inputs to lead to good or bad outcomes as a learning device.

Unexpected inputs

There is a military saying that your battle plan will start coming apart at the moment of engagement, and this is a good principle to impart into simulations. As one example, I helped create an emergency response exercise involving a tornado disaster where participants needed to organize response to the disaster; at different points in the scenario, a new development required them to quickly change direction, just like in real life. These unexpected events help teach about the situation itself and are memorable lessons.

Debrief and analysis

As a wise man once told me, experience itself is not the best teacher; reflected experience is. A post-simulation debrief and analysis after the simulation is critical to learning. Case in point: I was part of a faculty teaching conference at a medical school where a video was shown of a student in a simulation that required him to give a patient that she had cancer. The student made some mistakes and so received feedback afterward on what he did well and poorly. He correctly gave the information to the women straight without beating around the bush, didn’t make recovery promises that he couldn’t keep, and directed her to counseling. Then the student was told what he did not do well, such as giving off a fatalistic air in his voice and body language, repeatedly saying “I’m sorry,” crossing his arms over his chest, and looking down. He also used technical language such as “small-cell cancer” without explaining it. (As a general practice, backward-facing evaluation like this should be followed by forward-facing information on what the student can do differently to improve future performance, as this article discusses).

Simulations are time-consuming to create, and so it behooves faculty to ensure that the time invested produces learning outcomes beyond what would come from a simple quiz or worksheet. Following a few principles of simulation design will lead to a valuable learning experience.

Reference

Shellman, S. M., & Turan, K. (2006). Do simulations enhance student learning? An empirical evaluation of an IR simulation. Journal of Political Science Education, 2(1), 19–32. https://doi.org/10.1080/15512160500484168