As an instructional designer, I’ve often wanted to create branched simulations. Why? Well, with branching, we can adapt the content to each learner’s choices and then demonstrate the consequences of those choices. For example, we can allow learners to select a sub-optimal treatment option and then show them the effects that this therapy will have on their patient. In this virtual environment, prescribing the wrong treatment could harm, or even kill, the patient. It is through such failures (or successes), that learners gain their own insights. This can be far more effective than simply correcting learners’ poor decisions and keeping them on the linear path. Whenever we can situate learning within the realistic context of a story and empower learners to make their own decisions, there is a much greater chance for learning.
Before working with DecisionSim, however, I rarely got the opportunity to create branching simulations. Why? They can be time consuming and costly to develop. With DecisionSim, however, branching is now a viable option since we’ve eliminated the need for programmers.
So, we’ve been developing a variety of instructional approaches using different branching techniques. We’ve found that branching can be used not only to send learners down alternate learning paths, but it can also provide personalized feedback, and even incorporate some gaming into a simulation. This can be done with simple branching nodes (screens) or with more sophisticated rules and counters.
For example, we used branching to provide delayed consequences. In this approach, learners make a decision, but only see the consequences of that decision later in the simulation. This mirrors a realistic problem-solving situation where learners make several decisions but only later see a sub-optimal or optimal outcome. Through reflection, learners have to retrace their steps and determine the cause of this outcome.
To personalize feedback, we use branching to first assess learners without providing any remediation. Depending upon how learners perform on the assessment question, learners proceed down different branches. Recently, one of our clients used this technique in a diagnostic simulation. The assessment question asked learners to identify 3 out of 6 clinically significant findings that would support their diagnosis. If learners fail to identify the 3 optimal choices, they are sent back to the same question to try it again. This time, however, they are provided with hints—or scaffolding. If, however, they select all optimal as well as sub-optimal choices, they are sent to a remediation screen that sorts the answers into the two categories (clinically and non-clinically significant). If learners identify all 3 optimal choices only, then they see a congratulatory screen and move forward in the simulation. Through this branching technique, we are providing mastery-based learning.
These are just a few of our branching sequences. To learn more, download our PPT presentation, Branching Simulation Designs for Virtual Patients