The simulation complexity and the support method Essay
The simulation complexity and the support method
This study is expected to show that learning results in simulation-based learning environments are influenced strongly by the target group, the simulation complexity and the support method. In the case where knowledge acquisition is the criterion, the support method “direct access to knowledge domain” (DI-GES) proves to be superior to “guided exploratory search” in general. But the interaction between support method, simulation complexity and target group makes it quite clear that apprentices have greater problems in identifying the underlying formal structure of the simulation than students.
Engineering students might be more capable of and/or used to realizing that a simulation can be described by an algorithm which closely resembles a mathematical equation, thus making it easier to infer one of these. For apprentices, it would be better to be provided with direct access to the knowledge domain, instead of being supported with the design of experiments in both simulations. In the low-complexity simulation, it does not matter to the student sample whether they are given the information explicitly or have to infer it for themselves. But the DI-GES condition supports apprentices less than students.
Apprentices were unable to profit from the direct access to the knowledge domain as much in the more complex simulation as the students did. Whereas students were able to remember and reproduce many of the relationships correctly, this was not so true of the apprentices. To conclude: learners who are more highly educated will benefit more from experiential learning situations than those who are less so. The control groups who took the knowledge test immediately after the learning phase had higher scores than the groups who performed the knowledge test only after finishing the transfer task.
One would have expected subjects to use the transfer task for further knowledge acquisition, which would presumably have resulted in a better knowledge test score than that in the CG. But the reverse effect (although a rather small one) occurred. Independently of target group, members of both EGs could not remember the information given to them as well as the control group subjects. Not only did they fail to acquire additional knowledge during the transfer task, but were not even as good as those in the CG, which means that performing the transfer task led to a knowledge test score deterioration.
On one hand, it might be assumed that this is a simple fault of memory due to the fact that in the case of the EGs more time elapsed between learning and testing than in that of the CGs. On the other hand, it might be due to the fact that learners were confused by having to work with the transfer tasks, leading to a changed mental model and hypothesis as to the nature of the simulation structure. There was no learning from experience in the EG groups. The transfer performance was mainly influenced by the simulation complexity and the target group.
That means that the complexity of the simulation itself leads to a lower performance independently of the learning method. This is due to the fact that it is more difficult to plan the right sequence of steps to reach the defined goal states. A solid knowledge base was shown to be a precondition for successful transfer performance, as the high correlation (–. 515**) demonstrates. Because students did, in fact, acquire more knowledge, they should also perform better in the transfer task, which was actually the case. Apart from the simulation difficulty, the target group explained 14% of the variance.
This also means that the support method influences the transfer task performance only indirectly through the acquired knowledge during the learning phase. As the regression shows, knowledge acquisition is best predicted by the support method, whereas the transfer task performance is predicted best by simulation complexity.
Target group is the second strongest predictor for both. The observation that simulation complexity strongly affected the task performance is due to the fact that ColorSim7 contains variables with “self-referring dynamics,” which implies an exponential growth of that variable once it was given a value larger than 1.
Simulation containing dynamics are difficult to control and demand high numerical abilities and skills in computing, anticipating or thinking ahead concerning future variable states. Interestingly, students in general invested more time in solving the transfer task than apprentices. One can assume that investing more time means that they spend more time planning each step carefully, anticipating and calculating the consequences, and thinking through possible alternatives for each step.
But time spent and transfer performance do not correlate in the student group in general, which means that there must have been students who could solve the transfer task well even without spending too much time; but there must also have been students who took a long time without solving the task properly.
Apprentices, on the other hand, showed a positive correlation between time spent and transfer task performance, which means that thinking about the problems carefully increased the probability of a good performance.
University/College: University of Arkansas System
Type of paper: Thesis/Dissertation Chapter
Date: 19 May 2017
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