In this article a proposal for a taxonomy of computer simulations to support learning processes is presented. To achieve this goal, distinguishing criteria of computer simulations are identified and categorized. The work has been done in order to facilitate research about the evaluation of learning tools. The taxonomy is mainly built to be suitable for this objective, but can be of general help in establishing a common terminology. Sharing of findings and discussions among researchers from different fields can therefore by stimulated. Copyright © 2000 John Wiley & Sons, Ltd.

Prefabricated computer-based simulations usually offer a user-friendly interface. This allows inexperienced users fast access to the simulation because they do not have to possess specific knowledge about simulation techniques. Thus, giving simulation models an easy-to-use interface increases the acceptance of the simulation tool and draws attention to it. Learners are not only able to examine the results of their decisions but also the causes of these results using powerful system dynamics diagramming techniques. This adds transparency to the former black-boxes, producing so-called transparent-box business simulators. This article reports on an experiment evaluating the relevance and effects of structural transparency. This experimental design also can be used to examine other types of business simulators. Hypotheses regarding the effectiveness of transparency were tested. Results show the necessity for further research and collaboration.Computer simulation models are powerful tools to support problem solving and learning processes. In particular, the iterative process of modeling and simulating-which leads to a simulation model, mapping the problem structure adequately and showing the behavior modes of reality-is important in promoting the understanding of complex systems. Partly because modeling and simulation require significant expertise, business simulators have been developed to allow easier access to a specified model and simulation through the implementation of a user-friendly interface. Although these devices are to a large extent designed as black-box simulators, they enable the user to experience the dynamics created by his or her policies and decisions and therefore facilitate learning about and understanding of complex systems. However, black-box simulators do not provide direct insight into the problem structure. There is no information about the internal feedback structure of the model available, and the users usually have not participated in the process of model development. Hence, black-box simulators are assumed to be of limited effectiveness and efficiency in supporting the learning and problem-solving capabilities.For several years, the idea of transparent-box simulation/gaming has been discussed in the system dynamics (SD) community. Adding features to provide structural AUTHORS' NOTE: Andreas Größler wishes to express his thanks to Pål Davidsen and J. Michael Spector for their help and encouragement in an earlier stage of the research project reported here.

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Background The prehospital identification of stroke patients with large-vessel occlusion (LVO), that should be immediately transported to a thrombectomy capable centre is an unsolved problem. Our aim was to determine whether implementation of a state-wide standard operating procedure (SOP) using the Los Angeles Motor Scale (LAMS) is feasible and enables correct triage of stroke patients to hospitals offering (comprehensive stroke centres, CSCs) or not offering (primary stroke centres, PSCs) thrombectomy. Methods Prospective study involving all patients with suspected acute stroke treated in a 4-month period in a state-wide network of all stroke-treating hospitals (eight PSCs and two CSCs). Primary endpoint was accuracy of the triage SOP in correctly transferring patients to CSCs or PSCs. Additional endpoints included the number of secondary transfers, the accuracy of the LAMS for detection of LVO, apart from stroke management metrics. Results In 1123 patients, use of a triage SOP based on the LAMS allowed triage decisions according to LVO status with a sensitivity of 69.2% (95% confidence interval (95%-CI): 59.0–79.5%) and a specificity of 84.9% (95%-CI: 82.6–87.3%). This was more favourable than the conventional approach of transferring every patient to the nearest stroke-treating hospital, as determined by geocoding for each patient (sensitivity, 17.9% (95%-CI: 9.4–26.5%); specificity, 100% (95%-CI: 100–100%)). Secondary transfers were required for 14 of the 78 (17.9%) LVO patients. Regarding the score itself, LAMS detected LVO with a sensitivity of 67.5% (95%-CI: 57.1–78.0%) and a specificity of 83.5% (95%-CI: 81.0–86.0%). Conclusions State-wide implementation of a triage SOP requesting use of the LAMS tool is feasible and improves triage decision-making in acute stroke regarding the most appropriate target hospital.