Business Simulation Learning Journey
Here I explore how to create a business simulation's learning journey that delivers learning and engagement effectively and efficiently.
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In the early 1990's I started to reflect on my previous twenty plus years running simulations in the classroom during which I used them more than 2000 times with tens of thousands of business people. With a degree in Electrical Engineering I saw a parallel with servomechanisms (Hall & Cox, 1993). That is to say both servo mechanisms and business simulations are dynamic feedback processes. Initially, I identified two dynamics - cognition (understanding) and affection (feelings) (Hall & Cox, 1993). I then added a third dynamic - workload (cognitive load) (Hall, 1996).
Although above these dynamics are separate, they are not independent and interact. So besides workload and understanding the learning journey design must consider the impact on feelings (engagement). For example, too much or too little work will lead to disaffection. Confusion cause by a too difficult simulation or a simplistic simulation will lead to disaffection as will irrelevant learning.
During the learning journey new issues are introduced on a timely basis to increase workload (improve learning efficiency) and add to understanding (improve learning effectiveness). These introductions can either be designed into the business simulation (economic calibration and ramped complexity) or introduced on an ad hoc basis by the tutor (tutor intervention).
As shown above, a basic simulation without economic calibration, ramped complexity and tutor intervention, workload starts high and then falls. This means that the amount of learning is constrained.
Economic Calibration causes simulation difficulty to increase as time passes, increasing the amount of learning. For example a simulation might start in a cash rich situation but as participants grow markets and profits the need for capital investment and working capital impact liquidity. Consequentially, participants must improve their forecasts and cash management..
Ramped Complexity means that additional decisions, reports, issues and challenges are introduced as the simulation progresses.
Tutor Intervention involves the tutor proactively intervening to challenge and coach participants driving learning forward.
Economic and Ramped Complexity are explored in detail in Temporal Topical Progressions
Tutor Intervention is explored in detail in Tutor Support
Designing the learning journey involves designing the time-table and deciding the timing of issues and tasks taking into account how cognition (understanding), affection (feelings) and workload (cognitive load) change as the simulation progresses.
These involve participating teams working synchronously making decisions at preset times. As illustrated above the time table consists of a long session at the start of the simulation during which the participants prepare, become familiar and make their decisions for the first period. During the second period participants are faced with becoming more familiar with business reports and beginning to identify the impact of their decisions and this requires a longer than normal decision period. Later, as the simulation progresses and assuming no issues are introduced, decision periods can be shortened.
With these simulations the time table is managed by the tutor and thus, he or she can lengthen or shorten time periods.
Non interactive, Direct Use Simulations
These involve participating teams working asynchronously making decisions when they want. Because of this there is no need to time table on a period-by-period basis. Rather you must allow sufficient time in total for the preparation, familiarisation and decision-making. Experience running one Direct Use simulation with some two hundred teams showed that there were two patterns. One group started with a long initial planning period and then their decisions speeded up. The second group started decision-making quickly and then slowed as they found the task harder than they expected. Neither group seemed to learn more, be more engaged or be more successful.
With these simulations the time table is managed by the participants and thus the tutor may need to encourage the slow groups and challenge the fast groups. Using large charts to share key decisions and results between participants helps synchronise decision making.
Issue and Task timing is central to the design of the learning journey. It involves deciding which and when issues are introduced during the simulation and the way the issue is explored through qualitative feedback, results, decisions and tasks.
Issue and Task Timing is explored in more depth in Temporal Topical Progressions
Impact of Issues
Where issues (feedback, reports, decisions and tasks) are introduced during the simulation then time need to be planned to handle these. Below is a diagram showing estimates of the time needed to deal with issues as they were introduced.
This involves providing qualitative feedback either on a pre-planned basis or proactively as the simulation progresses.
Pre-planned feedback - Cognitive Prompts
Here the feedback is provided at pre-set points in the simulation.
To the right is an example is a comment, from my Product Launch simulation to encourage participant to prompt discussion on buying patterns and how this might impact demand.
Proactive feedback - Reflection Triggers
Here the feedback is determined by the business situation and may either be automatically generated by the simulation or provided on an ad hoc basis by the tutor.
To the right is an example reflection triggers from my Management Challenge simulation designed to get participants to reflect on business weaknesses.
This involves introducing new reports as the simulation progresses - reports that introduce new learning and encourage discussion.
The report to the right is introduced part way through my Distrain simulation to get discussion about profitability on a market sector basis.
This involves introducing decisions (and, usually, reports showing the impact) as the simulation progresses.
The example to the right shows how decisions were introduced in my Training Challenge simulation.
This involve injecting tasks at key points in the simulation. They may be automatically introduced at preset points by the simulation or on an ad hoc basis by the tutor.
The example to the right are a list of reports for participants to produce for their "parent company" (the tutor) that are introduced automatically by the Prospector Simulation.
Most recent update: 16/07/15
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