This study invited small groups to make several arguments by analogy about simple machines. Groups were first provided training on analogical (structure) mapping and were then invited to use analogical mapping as a scaffold to make arguments. In making these arguments, groups were asked to consider three simple machines: two machines that they had built, used, and made measurements with and one that they had not yet studied. Finally, groups were to produce an argument in favor of the machine that worked most like another machine. Seven of these approximately 50‐minute analogical‐mapping‐based comparison activities were given to 55 preservice elementary teachers working in 15 small groups over 7 weeks. When used as a scaffold for argumentation in small groups, these activities were found to generate a need for discernment, which allowed for simple machines and their parts to be understood in and connected to the context.
The project the authors of this paper are involved in is titled "System for intelligent realtime timetable optimization and monitoring". The objective is to develop a system being able to use delay-predictions for real-timedelay-monitoring, and in the long term, for a timetableoptimization in the range of train networks. The presented paper deals with the part of the system responsible for processing existing delays in the network to generate delaypredictions for depending trains in the near future. Therefore a rule-based system was developed, processing a set of predefined rules with the input of a specific delay in a deterministic manner, delivering a resulting delay-scenario as output. This rule-based system was used as a comparison to the specially developed neural network in order to evaluate the accuracy and faculty of abstraction of such an artificially intelligent component. An excerpt of the real train network of the Deutsche Bahn was the basis for this research, for simulation purposes we used the SNNS (Stuttgart Neural Network Simulator) [5]. At the end of this paper we can draw a conclusion in favour of the neural network, which is able to abstract from known delay constellations.
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