Sure, ShadowBox Training is great as is, but how is it when paired with a high-fidelity simulator? How about if I made my scenarios more generic; would they still be helpful?
Dr. Gary Klein and Joseph borders sought to answer two questions raised from their Shadowbox pilot project: (1) can Shadowbox be used with a high-fidelity simulator and (2) can the Shadowbox exercises be generic rather than unit specific. Use of Shadowbox with a high-fidelity simulator resulted in a 28% improvement in operator decision making. A web-based Shadowbox for a polyethylene unit was created to test operators from that unit and similar, but not identical, units. Testing of the latter had to be halted due to union objections regarding “testing”.
Can the ShadowBox approach be generalized so that scenarios are applicable beyond a specific refinery? In other words, can a scenario developed for one refinery have value when applied at a different location (e.g., refinery, company, etc.)? While specific knowledge may not transfer, is there general skills/knowledge that would make the training beneficial?
Can the ShadowBox approach help identify levels of expertise or faulty mental models?
How does the ShadowBox technique fit in as part of an overall console operator training tool?
A pilot effort was conducted to determine if the ShadowBox training technique, currently being developed for the military and civilian disaster response teams, would be a method to both capture expertise and enhance operator training. The results were promising with the development of two ShadowBox exercises for Marathon’s Detroit Refinery. However, there were still questions as to the use of the ShadowBox technique.
1. FCC theme matrix. This will contain 4-6 common FCC problems (e.g. water in the feed) and 4-6 cognitive challenges that FCC board operators face (e.g. ambiguous data).
2. ShadowBox Training scenarios. MacroCognition LLC will develop ShadowBox training scenarios using interviews with experienced FCC operators. We will construct these scenarios around the common problems list. Within each scenario, we will try to address multiple cognitive challenges. Each common problem and cognitive challenge will be applied at least once. We expect to revisit previously created ShadowBox scenarios and DMX training materials; and generate 3-4 new ShadowBox scenarios.
- a. Expert rankings and rationale for each decision point
- b. Accompanying visuals for each ShadowBox scenario
3. Console training program assessment. MacroCognition LLC will review 2 console-training programs and identify any cognitive gaps in the protocol.
4. Expert vs. novice performance differences for refinery vs. non-refinery specific scenarios.
5. Methods to utilize ShadowBox training method with high fidelity ethylene plant simulator.
6. Template and concept map of low fidelity simulator using ShadowBox training method.
7. Written and oral report of results at the completion of the project.